M 325K-H Honors Discrete Mathematics

Taught in the Spring semester by Prof. Michael Starbird, the course covers topics in mathematics rarely seen in undergraduate courses, such as countability.

M 325K Discrete Mathematics

Prerequisite: None. Faculty should not assume that students have taken calculus before taking M 325K. This is the first course that emphasizes understanding and creating proofs. Therefore, it provides a transition from the problem-solving approach of calculus to the entirely rigorous approach of advanced courses such as M365C or M373K. The number of topics required for coverage has been kept modest to allow adequate time for students to develop theorem-proving skills.

Course Topics:

• Fundamentals of logic
• Elementary Number theory
• Sequences and Mathematical Induction
• Set Theory

M 328K-H Honors Number Theory

Tthis course will move at a more rapid pace than M328K, in order to explore some advanced topics. Every Friday, students will also work on the development and presentation of proofs.

Students apply for this course by contacting the Math Advising Center in PMA 4.101, during Fall registration.

M 326K Foundations of Number Systems

Prerequisite and degree relevance: M 408D, M 408L, or M 408S with a grade of at least C-.Restricted to students in a teacher preparation program or who have the consent of the instructor. 

Course description: Study of number-related topics in middle-grade and secondary school mathematics. Topics include place value; meanings of arithmetic operations; analysis of computation methods; historical development of number concepts and notation; and rational, irrational, algebraic, transcendental, and complex numbers. Emphasis is on communicating mathematics, developing a pedagogical understanding of concepts and notation, and using both informal reasoning and proof. Three lecture hours a week for one semester.

M 328K Introduction to Number Theory

Prerequisite and degree relevance: Mathematics 325K or 341 with a grade of at least C-. This is a first course that emphasizes understanding and creating proofs; therefore, it must provide a transition from the problem-solving approach of calculus to the entirely rigorous approach of advanced courses such as M365C or M373K. The number of topics required for coverage has been kept modest so as to allow instructors adequate time to concentrate on developing the students theorem-proving skills.

Course description: The following subjects are included:

• Divisibility: divisibility of integers, prime numbers and the fundamental theorem of arithmetic.
• Congruences: including linear congruences, the Chinese remainder theorem, Euler's j-function, and polynomial congruences, primitive roots.

The following topics may also be covered, the exact choice will depend on the text and the taste of the instructor.

• Diophantine equations: (equations to be solved in integers), sums of squares, Pythagorean triples.
• Number theoretic functions: the Mobius Inversion formula, estimating and partial sums z(x) of other number theoretic functions.
• Approximation of real numbers by rationals: Dirichlet's theorem, continued fractions, Pells equation, Liouville's theorem, algebraic and transcendental numbers, the transcendence of e and/or z.

M 329F Theory of Interest

Prerequisite and degree relevance: Mathematics 408D, 308L, 408L, or 408S with a grade of at least C-.  This course covers the content for the SOA Exam FM. Topics include nominal and effective interest and discount rates, general accumulation functions and force of interest, yield rates, annuities including those with non-level payment patterns, amortization of loans, sinking funds, bonds, spot and forward rates, interest rate swaps, duration, and immunization.

M 329W Cooperative Mathematics

Prerequisite and degree relevance: Application through the College of Natural Sciences Career Design Center; Mathematics 408D, 408L, or 408S with a grade of at least C-; a grade of at least C- in two of the following courses: Mathematics 325K, 427J or 427K, 341, 362K, or 378K; and consent of the undergraduate adviser. Forty laboratory hours a week for one semester. 

Course Description: This course covers the work period of mathematics students in the Cooperative Education program, which provides supervised work experience by arrangement with the employer and the supervising instructor. The student must repeat the course each work period and must take it twice to receive credit toward the degree; at least one of these registrations must be during a long-session semester. No more than three semester hours may be counted toward the major requirement; no more than six semester hours may be counted toward the degree. The students first registration must be on a pass/fail basis.

M 427J Differential Equations with Linear Algebra

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C-. Mathematics 427J and 427K may not both be counted.

Course description: M 427J is a basic course in ordinary and partial differential equations, emphasizing the connection with linear algebra and systems of equation.  Other subject matter will include an introduction to vector spaces, linear operators and eigenvalues, systems of linear differential equations, introduction to partial differential equations and Fourier series. The course meets three times a week for lecture and twice more for problem sessions.  Five sessions a week for one semester.

M 427K-H Honors Advanced Calculus For Applications I

This course is taught in the Spring semester; it provides greater depth than the usual differential equations course, has less emphasis on computation, more conceptual material.  Each professor chooses additional topics, based on their research interests.

M 427K Advanced Calculus for Applications I

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C-. Mathematics 427J and 427K may not both be counted.

Course description: M 427K is a basic course in ordinary and partial differential equations, with Fourier series. It should be taken before most other upper-division, applied mathematics courses. The course meets three times a week for lectures and twice more for problem sessions. Geared to the audience primarily consisting of engineering and science students, the course aims to teach the basic techniques for solving differential equations that arise in applications. The approach is problem-oriented and not particularly theoretical. Most of the time is devoted to first and second order ordinary differential equations with an introduction to Fourier series and partial differential equations at the end. Depending on the instructor, some time may be spent on applications, Laplace transformations, or numerical methods. Five sessions a week for one semester.

M 427L-AP Honors Advanced Calculus for Applications II

Course description:  Rather than cover material of a standard calculus class, this course goes directly into an upper-division treatment of multivariable calculus and covers this topic from a more advanced perspective. In addition, M427L-AP covers additional material, such as theorems of Stokes, Green, and Gauss, that are not found in general calculus. This material prepares students for advanced engineering (eg., fluid dynamics, and aerodynamics), physics (eg., electricity and magnetism, quantum field theory), and mathematics (eg., differential topology, Lie groups, etc.). Together with AP BC credit, this course meets the calculus requirement in all Colleges and gives the student upper-division math hours.

M 427L Advanced Calculus for Applications II

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C-.

Course description: Topics include matrices, elements of vector analysis and calculus functions of several variables, including gradient, divergence, and curl of a vector field, multiple integrals, and chain rules, length and area, line and surface integrals, Greens theorem in the plane and space. If time permits, topics in complex analysis may be included. This course has three lectures and two problem sessions each week.  Five sessions a week for one semester.

M 139S Seminar on Actuarial Practice

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions. 

M 333L Structure of Modern Geometry

Prerequisite and degree relevance: None. Faculty should not assume that students have taken calculus before taking M 333L. These requirements are set to ensure mathematical maturity, rather than for content knowledge. They may be waived by the instructor in some cases, most notably a specialization in mathematics. M 333L is required for students seeking certification to teach secondary school mathematics. 

Course description: The course is designed to familiarize prospective mathematics teachers with the geometrical concepts which relate to two and three-dimensional geometry and the mathematical techniques used in the study of geometry. The emphasis is both on the development of the understanding of the concepts and the ability to use the concepts in proving theorems. The course includes the study of axiom systems, transformational geometry, and an introduction to non-Euclidean geometries, supplemented by other topics as determined by the instructor. While the course is primarily designed for teachers, its content and approach may be of interest to other students of mathematics.

M 339D Introduction to Financial Mathematics for Actuaries Syllabus

Prerequisite and degree relevance: Actuarial Foundations 329 or Mathematics 329F; and Mathematics 362K with a grade of at least C-. Moreover, the instructor advises that students will need a thorough understanding and operational knowledge of (at least) calculus, finite-stage-space probability, and the term structure of interest rates.

Course description: This course is intended to provide the mathematical foundations necessary to prepare for a portion of

     (1) the joint SOA/CAS exam FM/2, as well as

     (2) the SoA exam MFE and the financial economics portion of the CAS Exam 3.

Additionally, the course is aimed at building up the vocabulary and the techniques indispensable in the workplace at current financial and insurance institutions. This is not an exam-prep seminar. There is intellectual merit to the course beyond the ability to prepare for a professional exam.

The material exhibited includes elementary risk management, forward contracts, options, futures, swaps, the simple random walk, the binomial asset pricing model, and its application to option pricing. The remainder of the Exam MFE/3F curriculum is exhibited in course M339W (also offered by the Department of Mathematics).

M 339J Probability Models with Actuarial Applications

Prerequisite and degree relevance: Mathematics 358K or 378K with a grade of at least C-.

Course description: Introductory actuarial models for life insurance, property insurance, and annuities. With Mathematics 349P, covers the syllabus for the professional actuarial exam on model construction.

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions.

M 339U Actuarial Contingent Payments I

Prerequisite and degree relevance: Mathematics 362K with a grade of at least C-; credit with a grade of at least C- or registration for Actuarial Foundations 329 or Mathematics 329F; and credit with a grade of at least C- or registration for Mathematics 340L or 341.

Course description: Intermediate actuarial models for life insurance, property insurance, and annuities.

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions. 

M 339V Actuarial Contingent Payments II

Prerequisite and degree relevance: Actuarial Foundations 329 or Mathematics 329F, and Mathematics 339U with a grade of at least C- in each.

Course description: Advanced actuarial models for life insurance, property insurance, and annuities.

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions.

M 339W Financial Mathematics for Actuarial Applications

Prerequisite and degree relevance: Mathematics 339D with a grade of at least C-.

Course description: Pricing, stock price, and interest rate models for actuarial applications. Tools include lognormal distribution, Brownian motion, Black-Scholes, and delta hedging.

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions. 

M 340L Matrices and Matrix Calculations

Prerequisite and degree relevance: Mathematics 408C, 408K, or 408N with a grade of at least C-. Only one of the following may count: Mathematics 340L, 341, Statistics and Data Sciences 329C, or Statistics and Scientific Computation 329C.

Course description: The goal of M 340L is to present the many uses of matrices and the many techniques and concepts needed for such uses. The emphasis is on concrete concepts and understanding and using techniques, rather than on learning proofs and abstractions. The course is designed for applications-oriented students such as those in the natural and social sciences, engineering, and business. Topics might include matrix operations, systems of linear equations, introductory vector-space concepts (e.g., linear dependence and independence, basis, dimension), determinants, introductory concepts of eigensystems, introductory linear programming, and least square problems.

M 341-H Honors Linear Algebra

Taught in the Spring semester by Prof. Ronny Hadani, the course covers linear algebra from an advanced perspective. You can view the Spring 2009 syllabus here.

Students apply for this course by contacting the Math Advising Center in PMA 4.101, during Fall registration.

M 341 Linear Algebra and Matrix Theory

Formerly M 311.

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C-. Restricted to mathematics majors. Only one of the following may count: Mathematics 340L, 341, Statistics and Data Sciences 329C, or Statistics and Scientific Computation 329C. Majors with a 'math' advising code must register for M 341 rather than for M 340L; majors without a 'math' advising code must register for M 340L. Math majors must make a grade of at least C- in M 341.

Course description:  The emphasis in this course is on understanding the concepts and learning to use the tools of linear algebra and matrices. Some time should be devoted to teaching students to do proofs. The fundamental concepts and tools of the subject covered are:

• Matrices: matrix operations, the rules of matrix algebra, invertible matrices.
• Linear equations: row operations and row equivalence; elementary matrices; solving systems of linear equations by Gaussian elimination; inverting a matrix with the aid of row operations.
• Vector spaces: vector spaces and subspaces; linear independence and span of a set of vectors, basis and dimension; the standard bases for common vector spaces.
• Inner product spaces: Cauchy-Schwarz inequality, orthonormal bases, the Gramm-Schmidt procedure, orthogonal complement of a subspace, orthogonal projection.
• Linear Transformations: kernel and range of a linear transformation, the Rank-Nullity Theorem, linear transformations and matrices, change of basis, similarity of matrices.
• Determinants: the definition and basic properties of determinants, Cramer's rule.
• Eigenvalues: eigenvalues and eigenvectors, diagonalizability of a real symmetric matrix, canonical forms.

M 343K Introduction to Algebraic Structures

Prerequisite and degree relevance: Either consent of Mathematics Advisor or two of the following courses with a grade of at least C- in each: Mathematics 325K or Philosophy 313K, Mathematics 328K, Mathematics 341. This course is designed to provide additional exposure to abstract rigorous mathematics on an introductory level. Students who demonstrate superior performance in M 311 or M 341 should take M 373K instead of 343K. Those students whose performance in M 311 or M 341 is average should take M 343K before taking M 373K. Credit for M 343K can NOT be earned after a student has received credit for M 373K with a grade of at least C-.

Course description: Elementary properties of groups and rings, including symmetric groups, properties of the integers, polynomial rings, elementary field theory.

M 343L Applied Number Theory

Prerequisite and degree relevance:  Mathematics 328K or 343K with a grade of at least C-.

Topics: Basic properties of integers, including properties of prime numbers, congruences, and primitive roots. introduction to finite fields and their vector spaces with applications to encryption systems and coding theory. Three lecture hours a week for one semester.

M 343M Error-Correcting Codes

Prerequisite and degree relevance: Mathematics 328K or 341 with a grade of at least C-.

Course description: Introduction to applications of algebra and number theory to error-correcting codes, including finite fields, error-correcting codes, vector spaces over finite fields, Hamming norm, coding, and decoding

M 344K Intermediate Symbolic logic

Prerequisite and degree relevance: Philosophy 313, 313K, or 313Q.

Course description: Same as Philosophy 344K. A second-semester course in symbolic logic: formal syntax and semantics, basic metatheory (soundness, completeness, compactness, and Löwenheim-Skolem theorems), and further topics in logic. 

M 346 Applied Linear Algebra

Prerequisite and degree relevance: The prerequisite is M 341 (or M 311) or M 340L with a grade of C- or better.

Course description: Emphasis on diagonalization of linear operators and applications to discrete and continuous dynamical systems and ordinary differential equations. Other subjects include inner products and orthogonality, normal mode expansions, vibrating strings, and the wave equation, and Fourier series.

M 348 Scientific Computation in Numerical Analysis

Prerequisite and degree relevance: Computer Science 303E or 307, and Mathematics 341 or 340L with a grade of at least C-.

Course description: Introduction to mathematical properties of numerical methods and their applications in computational science and engineering. Introduction to object-oriented programming in an advanced language. Study and use of numerical methods for solutions of linear systems of equations, non-linear least-squares data fitting, numerical integration of multi-dimensional, non-linear equations and systems of initial value ordinary differential equations.

M 349P Actuarial Statistical Estimates I

Prerequisite and degree relevance: Mathematics 339J, and 341 or 340L, with a grade of at least C- in each. Please note that thorough knowledge of calculus, probability, and statistics will be assumed.

Course description: Statistical estimation procedures for random variables and related quantities in actuarial models. Together with Mathematics 339J, M 349P covers the syllabus for the professional actuarial exam on model construction.

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions.

M 349R Applied Regression and Time Series

Prerequisite and degree relevance:  Mathematics 339J or 339U, and 358K or 378K, with a grade of at least C- in each.

Course description: Introduction to simple and multiple linear regression and to elementary time-series models, including auto-regressive and moving-average models. Emphasizes fitting models to data, evaluating models, and interpreting results

Actuarial Course descriptions and guides in selecting actuarial courses are available at the Actuarial Course Descriptions. 

M 358K Applied Statistics

Prerequisite and degree relevance: M 362K with a grade of C- or better. This course is intended for students planning to teach secondary mathematics, students working for a BA in mathematics, and students in the natural sciences. Students preparing for graduate work in mathematical statistics should take M 378K instead of or after taking this course. 

Course Description: This is a first course in applied statistics, building on previous experience with probability. Emphasis will be on the development of statistical thinking and working with real data. Topics include: exploratory data analysis, regression and correlation, introduction to planning and conducting surveys and experiments, sampling distributions, confidence intervals (for proportions, means, differences between proportions, differences between two means paired and unpaired), and tests of significance (for proportions, means, differences between proportions, differences between means; chi-squared test; one and two sample t procedures; inference for slope of least squares line; analysis of variance). Students will be expected to apply the statistical ideas they learn in one or more projects. The statistical software Minitab will be used extensively in the course.

M 360M Mathematics as Problem Solving

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C- and written consent of instructor.

Course description:  Discussion of heuristics, strategies, and methods of problem-solving, and extensive practice in both group and individual problem-solving. Communicating mathematics, reasoning, and connections amongst topics in mathematics are emphasized.

M 361 Functions of A Complex Variable

Prerequisite and degree relevance: Mathematics 427J, 427K, or 427L with a grade of at least C-.

Course description: M 361 consists of a study of the properties of complex analytic functions. Students are mainly from physics and engineering, with some mathematics majors and joint majors. Representative topics are Cauchy's integral theorem and formula, Laurent expansions, residue theory and the calculation of definite integrals, analytic continuation, and asymptotic expansions. Rigorous proofs are given for most results, with the intent to provide the student with a reliable grasp of the results and techniques.

M 361K Intro to Real Analysis

Prerequisite and degree relevance: Either consent of the Undergraduate Mathematics Faculty Advisor or two of the following courses with a grade of at least C- in each: Mathematics 325K or Philosophy 313K, Mathematics 328K, Mathematics 341. Students who have received a grade of C- or better in Mathematics 365C may not take Mathematics 361K.

Course description: This is a rigorous treatment of the real number system, of real sequences, and of limits, continuity, derivatives, and integrals of real-valued functions of one real variable.

M 362K Probability I

Prerequisite and degree relevance: Mathematics 408D, 408L, or 408S with a grade of at least C-. Mathematics 362K and Statistics and Scientific Computation 321 may not both be counted.

Course description: An introductory course in the mathematical theory of probability, fundamental to further work in probability and statistics, includes basic probability properties, conditional probability and independence, various discrete and continuous random variables, expectation and variance, central limit theorem, and joint probability distributions.

M 362M Introduction to Stochastic Processes

Prerequisite and degree relevance: Mathematics 362K with a grade of at least C-. 

Course description: Introduction to Markov chains, birth and death processes, and other topics.

M 364K Vector and Tensor Analysis

Prerequisite and degree relevance: The prerequisite is M 427K or M 427L, with a grade of at least C. 

Course description: Topics include vector algebra and calculus, integral theorems, general coordinates, invariance, tensor analysis, and perhaps an introduction to differential geometry. It is anticipated that a significant percentage of students will be physics majors. 

M 365C Real Analysis I

Prerequisite and degree relevance: Either consent of the Undergraduate Mathematics Faculty Advisor or two of the following courses with a grade of at least C- in each: Mathematics 325K or Philosophy 313K, Mathematics 328K, Mathematics 341. Students who receive a grade of C- in one of the prerequisite courses are advised to take Mathematics 361K before attempting 365C. Students planning to take Mathematics 365C and 373K concurrently should consult a mathematics adviser.

Course description: This course is an introduction to Analysis. Analysis, together with Algebra and Topology, form the central core of modern mathematics. Beginning with the notion of limit from calculus and continuing with ideas about convergence and the concept of function that arose with the description of heat flow using Fourier series, analysis is primarily concerned with infinite processes, the study of spaces and their geometry where these processes act and the application of differential and integral to problems that arise in geometry, PDE, physics and probability. 

A rigorous treatment of the real number system, Euclidean spaces, metric spaces, continuity of functions in metric spaces, differentiation and Riemann integration of real-valued functions of one real variable, and uniform convergence of sequences and series of functions.

M 365D Real Analysis II

Prerequisite and degree relevance: Mathematics 365C with a grade of at least C-.

Course description: Recommended for students planning to undertake graduate work in mathematics. A rigorous treatment of selected topics in real analysis, such as Lebesgue integration, or multivariate integration and differential forms. 

Topics:

1. Geometry and topology of Rⁿ
2. Spaces of continuous functions
3. Lebesgue integration in one-variable
4. L^p spaces, Hilbert spaces, linear operators

Text: Davidson & Donsig: Real Analysis and Real Applications Prentice-Hall ISBN 0-13-041647-9 

M 365G Curves and Surfaces

Prerequisite and degree relevance: Credit with a grade of at least C- or registration for Mathematics 365C. Students should know multivariable calculus and a little linear algebra.

Course Description: Calculus applied to curves and surfaces in three dimensions: graphs and level sets, tangent spaces, vector fields, surfaces, orientation, the Gauss map, geodesics, parallel transport, the second fundamental form and the Weingarten map, length and curvature of plane curves, curvature of surfaces, the exponential map, and the Gauss-Bonnet Theorem.

Textbook:  Elementary Topics in Differential Geometry, John A. Thorpe, Springer--Verlag, New York, 1979. (ISBN 0-387-90357-7)

INTRODUCTION: Differential geometry is a rich and active area of research in pure mathematics. It also provides powerful tools for disciplines like general relativity and other branches of mathematical physics as well as for applications in engineering and computer graphics. This course will introduce the basic language and methods of differential geometry by studying the geometry of n-dimensional hypersurfaces in (n+1)-dimensional Euclidean space. The course would be excellent preparation for graduate courses in Differential Topology or Riemannian Geometry, as well as for further study in applications like those listed above.

M 367K Topology I

Prerequisite and degree relevance: Mathematics 361K or 365C or consent of instructor.

Course description: An introduction to topology, including sets, functions, cardinal numbers, and the topology of metric spaces

This is a course that emphasizes understanding and creating proofs. The number of topics required for coverage has been kept modest to allow instructors adequate time to concentrate on developing the students' theorem-proving skills. The syllabus below is a typical syllabus. Other collections of topics in topology are equally appropriate. For example, some instructors prefer to restrict themselves to the topology of the real line or metric space topology.

• Cardinality: 1-1 correspondence, countability, and uncountability.
• Definitions of topological space: basis, sub-basis, metric space.
• Countability properties: dense sets, countable basis, local basis.
• Separation properties: Hausdorff, regular, normal.
• Covering properties: compact, countably compact, Lindelof.
• Continuity and homeomorphisms: properties preserved by continuous functions, Urysohns Lemma, Tietze Extension Theorem.
• Connectedness: definition, examples, invariance under continuous functions.

Notes containing definitions, theorem statements, and examples have been developed for this course and are available. The notes include some topics beyond those listed above.

M 367L Topology II

Prerequisite and degree relevance: Mathematics 367K with a grade of at least C- or consent of instructor.

Course description: Various topics in topology, primarily of a geometric nature.

M 368K Numerical Methods for Applications

Prerequisite and degree relevance: Mathematics 348 with a grade of at least C-. Only one of the following may be counted: Computer Science 367, Mathematics 368K, Physics 329.

Course description: Continuation of Mathematics 348. Topics include splines, orthogonal polynomials, smoothing of data, iterative solution of systems of linear equations, approximation of eigenvalues, two-point-boundary value problems, numerical approximation of partial differential equations, signal processing, optimization, and Monte Carlo methods.

M 371E Learning Assistant Experience in Mathematics

Prerequisite and degree relevance: Mathematics 408C, 408K, 408N, 408R, or equivalent, and consent of instructor.

Course description: Students assist instructors and TAs in mathematics courses. This is a hands-on experience in what it is like to teach and support students in the learning of mathematics in undergraduate courses. Students must attend classroom training and discussions and work in Calculus discussion sections or undergraduate classrooms where mathematics is being taught. One class hour and three hours of fieldwork in an undergraduate mathematics course a week for one semester.

M 175 Conference Course

Prerequisite and degree relevance: Vary with the topic, and are given in the course schedule. May not be counted toward a degree in mathematics. A signed permission form must be submitted before registration. The forms are available in the department office (PMA 8.100) or the Mathematics, Physics, and Astronomy Advising Center.

Course description: It is the responsibility of the student to select a professor and make individual arrangements with the professor regarding the meeting, time, and course content. One, two, three, or four meetings a week for one semester.

M 175T Seminar for Prospective Teachers

N/A

M 275 Conference Course

Prerequisite and degree relevance: Vary with the topic, and are given in the course schedule. May not be counted toward a degree in mathematics. A signed permission form must be submitted before registration. The forms are available in the department office (PMA 8.100) or the Mathematics, Physics, and Astronomy Advising Center.

Course description: It is the responsibility of the student to select a professor and make individual arrangements with the professor regarding the meeting, time, and course content. One, two, three, or four meetings a week for one semester.

M 372K PDE and Applications

Prerequisite and degree relevance: Mathematics 427J or 427K with a grade of at least C-. One of M 361K or M 365C is also recommended.

Course description: Partial differential equations arise as basic models of flows, diffusion, dispersion, and vibrations. Topics include first and second order partial differential equations and classification, particularly the wave, diffusion, and potential equations, their origins in applications and properties of solutions, characteristics, maximum principles, Greens functions, eigenvalue problems, and Fourier expansion methods.

M 373K Algebraic Structures I

Prerequisite and degree relevance: Either consent of the Undergraduate Mathematics Faculty Advisor or two of the following courses with a grade of at least C- in each: Mathematics 325K or Philosophy 313K, Mathematics 328K, Mathematics 341. Students who receive a grade of C- in one of the prerequisite courses are advised to take Mathematics 343K before attempting 373K. Students planning to take Mathematics 365C and 373K concurrently should consult a mathematics adviser.

Course description: A study of groups, rings, and fields, including structure theory of finite groups, isomorphism theorems, polynomial rings, and principal ideal domains. 

M 373K is a rigorous course in pure mathematics. The syllabus for the course includes topics in the theory of groups and rings. The study of group theory includes normal subgroups, quotient groups, homomorphisms, permutation groups, the Sylow theorems, and the structure theorem for finite abelian groups. The topics in ring theory include ideals, quotient rings, the quotient field of an integral domain, Euclidean rings, and polynomial rings. This course is generally viewed (along with M 365C) as the most difficult of the required courses for a mathematics degree. Students are expected to produce logically sound proofs and solutions to challenging problems.

M 373L Algebraic Structures II

Prerequisite and degree relevance:  Mathematics 373K with a grade of at least C-. M 373L is strongly recommended for undergraduates contemplating graduate study in mathematics. 

Course description: Topics from vector spaces and modules, including direct sum decompositions, dual spaces, canonical forms, and multilinear algebra. 

M 373L is a continuation of M 373K, covering a selection of topics in algebra chosen from field theory and linear algebra. Emphasis is on understanding theorems and proofs. 

M 374 Fourier and Laplace Transforms

Prerequisite and degree relevance: Mathematics 427J or 427K with a grade of at least C-.

Course description: The course covers operational properties and applications of Laplace transforms and covers some properties of Fourier transforms. 

M 374G Linear Regression Analysis

Prerequisite and degree relevance: Mathematics 358K or 378K with a grade of at least C-, Mathematics 341 or 340L, and consent of instructor.

Course description: Fitting of linear models to data by the method of least squares, choosing best subsets of predictors, and related materials. 

M 374M Mathematical Modeling in Science and Engineering

Prerequisite and degree relevance: Mathematics 427J or 427K, and 340L or 341, with a grade of at least C- in each; and some basic programming skills.

Course description: Tools for studying differential equations and optimization problems that arise in the engineering and physical sciences. Includes dimensional analysis and scaling, regular and singular perturbation methods, optimization and calculus of variations, and stability.

M 375 Conference Course

Prerequisite and degree relevance: Vary with the topic, and are given in the course schedule. May not be counted toward a degree in mathematics. A signed permission form must be submitted before registration. The forms are available in the department office (PMA 8.100) or the Mathematics, Physics, and Astronomy Advising Center.

Course description: It is the responsibility of the student to select a professor and make individual arrangements with the professor regarding the meeting, time, and course content. One, two, three, or four meetings a week for one semester.

M 375C Conference Course

Prerequisite and degree relevance: Vary with the topic, and are given in the course schedule. A signed permission form must be submitted before registration. The forms are available in the department office (PMA 8.100) or the Mathematics, Physics, and Astronomy Advising Center.

Course description: It is the responsibility of the student to select a professor and make individual arrangements with the professor regarding the meeting, time, and course content. One, two, three, or four meetings a week for one semester.

M 375T Functions and Modeling

M 375T Functions and Modeling runs concurrently with M 315C Foundations, Functions, and Regression Models. This course is a mathematics research and report course that is restricted to Mathematics Education graduate students.

M 375T Analysis on Manifolds

M 375T Introduction to Graduate Studies in Mathematics

This is a mathematics research and report topics course supervised by a Mathematics Faculty member. Students work directly with a faculty member, identifying a research topic focused on effective teaching strategies. Faculty member approval, in writing, is required.

M 376C Methods of Applied Mathematics

Prerequisite and degree relevance: Mathematics 427J or 427K, and 340L or 341, with a grade of at least C- in each.

Course description: Variational methods and related concepts from classical and modern applied mathematics. Models of conduction and vibration that lead to systems of linear equations and ordinary differential equations, eigenvalue problems, initial and boundary value problems for partial differential equations. Topics may include a selection from diagonalization of matrices, eigenfunctions and minimization, asymptotics of eigenvalues, separation of variables, generalized solutions, and approximation methods. May be repeated for credit when the topics vary. 

M 378K Intro to Mathematical Statistics

Prerequisite and degree relevance: Mathematics 362K with a grade of at least C-. Same as Statistics and Data Sciences 378. Only one of the following may be counted: Mathematics 378K, Statistics and Data Sciences 378, Statistics and Scientific Computation 378. 

Students taking this course are usually majoring in mathematics, actuarial science, or one of the natural sciences. M362K, 358K, and 378K form the core sequence for students in statistics. 

Course description: Sampling distributions of statistics, estimation of parameters (confidence intervals, method of moments, maximum likelihood, comparison of estimators using mean square error and efficiency, sufficient statistics), hypothesis tests (p-values, power, likelihood ratio tests), and other topics. 

This is the first course in mathematical statistics and is taught from a classical viewpoint. The major topics are: estimation of parameters, including maximum likelihood estimation; sufficient statistics, and confidence intervals; testing of hypotheses including likelihood ratio tests and the Neyman Pearson theory; the distributions and other properties of some statistics that occur in sampling from normal populations such as the gamma, beta, chi-squared, Students t, and F distributions; and fitting straight lines. The course is designed to give students some insight into the theory behind the standard statistical procedures and also to prepare continuing students for the graduate courses. Within the limits of the prerequisites, students are expected to reproduce and apply the theoretical results; they are also expected to be able to carry out some standard statistical procedures. 

M 378N Generalized Linear Models

Prerequisite and degree relevance: Mathematics 378K with a grade of at least C- or consent of instructor. Mathematics 375T (Topic: Generalized Linear Models) and 378N may not both be counted.

Course description: Extensions to ordinary least-squares regression, including Poisson regression, the lasso, mixed models, and ridge regression.

M 379H Honors Tutorial Course

Prerequisite and degree of relevance: Admission to the Mathematics Honors Program; Mathematics 365C, 367K, 373K, or 374G with a grade of at least A-, and another of these courses with a grade of at least B-; and consent of the honors adviser.

Course description: Directed reading, research, and/or projects, under the supervision of a faculty member, leading to an honors thesis.

M 474M Mathematical Modeling in Science and Engineering

Prerequisite and degree relevance: Mathematics 427K, and 341 or 340L, with a grade of at least C- in each; and some basic programming skills. 

Course description: Tools for studying differential equations and optimization problems that arise in the engineering an physical sciences. Includes dimensional analysis and scaling, regular and singular perturbation methods, optimization and calculus of variations, and stability.

M 381C (Patrizi) Real Analysis

Please visit the prelim courses syllabi

M 382C (Sadun) Algebraic Topology

Please visit the prelim courses syllabi

M 383C (Arbogast) Methods of Applied Mathematics

Please visit the prelim courses syllabi

M 385C (Zitkovic) Theory of Probability

Please visit the prelim courses syllabi

M 387C (Engquist) Numerical Analysis: Algebra & Approx.

Please visit the prelim courses syllabi

M 390C (Ciperiani) Algebraic Number Theory

This will be an introductory course. We will study the ring of integers of a number field: prove the finiteness of the class group, prove Dirichlet's unit theorem, and analyze the decomposition of prime ideals when lifted to a bigger field. We will continue with a brief discussion of local fields and analytic methods in number theory. Finally we will conclude with an introduction to class field theory without proofs.

M 390C (Allcock) Arithmetic Groups

examples: SL(n,Z), Sp(2n,Z), orthogonal groups over Z

hyperbolic space, more general symmetric spaces, and applications in algebraic geometry.

finiteness of covolume; K(G,1) spaces.

residual finiteness, existence of finite-index torsion-free subgroups.

elements of algebraic groups (characteristic 0 only, quite concrete, the aim being to be able to give the full definition of arithmetic groups).

p-adic groups and the adelic perspective on arithmetic groups. Maximal subgroups of SL2R that are commensurable with SL2Z. The affine Bruhat-Tits building on which an arithmetic group acts, with applications.

Survey of strong approximation.

Prerequisites: familiarity with semisimple Lie groups, measure theory, group theory, finite algebraic extensions of Q, and the p-adic numbers.

Text: no official required text. But we will refer to Arithmetic Groups by Witte-Morris, and Algebraic Groups and Number Theory by Platonov and Rapinchuk, and other sources. Note that the Witte-Morris book is available for free online, and in an inexpensive print version.

M 391C (Bowen) Analytic Group Theory

Course Description: The goal of this course is for students to learn the background material needed to research modern analytic group theory. This includes: growth of groups (especially Gromov’s Polynomial Growth Theorem), amenability, Gromov hyperbolic groups, graphs of groups, Margulis’ super-rigidity, Normal Subgroup and Arithmeticity Theorems for higher rank lattices, random walks on groups, property (T), invariant random subgroups (IRS’s) and sofic groups. 

Prerequisites: students are expected to be familiar with measure theory, L^p spaces, smooth manifolds and fundamental groups. Otherwise prerequisites will be kept to a minimum. 

References:

Bekka, Bachir; de la Harpe, Pierre; Valette, Alain Kazhdan's property (T). New Mathematical Monographs, 11. Cambridge

Capraro, Valerio (NL-MATH); Lupini, Martino (1-CAIT)
Introduction to sofic and hyperlinear groups and Connes' embedding conjecture. 
With an appendix by Vladimir Pestov. Lecture notes in Mathematics, 2136. Springer, Cham, 2015. viii+151University Press, Cambridge, 2008. xiv+472 

Juschenko, Kate
Amenability of discrete groups by examples.
Mathematical Surveys and Monographs, 266. American Mathematical Society, Providence, RI, [2022], ©2022. xi+165 pp.

Woess, Wolfgang Random walks on infinite graphs and groups. Cambridge Tracts in Mathematics, 138. Cambridge University Press, Cambridge, 2000. xii+334 pp. 

Zimmer, Robert J. Ergodic theory and semisimple groups. Monographs in Mathematics, 81. Birkhäuser Verlag, Basel, 1984. x+209 pp. ISBN: 3-7643-3184-4

M 392C (Damiolini) Algebraic Geometry

One could define algebraic geometry as the discipline that studies varieties, that is spaces of solutions of polynomial equations. This course is a first introduction to the subject: as such we will discuss what we mean by varieties (and their more general cousins, schemes) and what are the tools that we can use to describe these geometric objects. The course will be built so that, towards the end of the class, we will be able to understand the Riemann-Roch theorem and some of its applications.

The prerequisites for this course are basic algebra (rings and modules) and point-set topology. We will use results from commutative algebras and some categorical language; both will be introduced/recalled during the course. If you are unsure if you satisfy these prerequisites, drop me an email!

Book: In the first part of the course, I plan to follow the book "Algebraic Geometry I: Schemes" by Görtz and Wedhorn; however, purchasing the book is not required. I will also use Gathmann's notes (https://agag-gathmann.math.rptu.de/de/alggeom.php) and Vakil's "The rising Sea" book (https://math.stanford.edu/~vakil/216blog/FOAGfeb2124public.pdf).

M 392C (Perutz) Lie Groups

Continuous symmetry groups are ubiquitous in mathematics. Linear-algebraic examples include the general and special linear groups, and the groups of linear isometries of inner products, Hermitian products and symplectic pairings. Then there are the groups of isometries of Euclidean and hyperbolic spaces, which appear physically as the Galilean and Lorentz groups of classical mechanics and special relativity; and the spinor groups first encountered in the study of electron spin.  

How should one treat continuous symmetries mathematically?  An effective method is to treat the symmetries as a Lie group, which is simultaneously a group and a smooth manifold, whose multiplication map is smooth. The theory of Lie groups grows naturally out of linear algebra. The fact that commuting diagonalizable matrices have simultaneous eigenspaces leads to a structural understanding of unitary groups, and eventually of all compact Lie groups. Lie’s insight was that the algebraic structure of the tangent space at the identity element, as a Lie algebra, determines almost everything about the Lie group. While a fully algebraic approach is therefore conceivable, the theory is richer – and closer to its applications - when one admits insights from differential geometry and occasionally from algebraic topology. 

This course will center on the classic theory of compact connected Lie groups, notably the principles that such groups can be classified by combinatorial “root data”; that their irreducible representations are classified by their highest weights;  and that there is a uniform formula for the characters of these representations. This is an example-driven subject, and I will encourage you to work through examples. By the end of the course, you should have a concrete understanding of groups such as SU(n). 

Prerequisites: The Algebraic Topology and Differential Topology prelims are more than sufficient. Algebra: Sound understanding of abstract linear algebra, basic group theory and topology are indispensable. Representation theory of finite groups is helpful but not assumed. Topology:  I will assume you know the rudiments of differential topology, e.g. fluency with vector fields on manifolds, and of algebraic topology, e.g. fundamental groups in relation to covering spaces. Knowledge of (co)homology is helpful but not vital. 

Main texts: J. F. Adams, Lectures on Lie Groups; T. Bröcker and T. tom Dieck, Representations of Compact Lie Groups; R. Carter, G. B. Segal and I. MacDonald, Lectures on Lie Groups and Lie Algebras (especially Segal’s part).

M 392C (Siebert) Moduli and invariants in symplectic and algebraic geometry

Description: The aim of this class is an introduction to the study of various moduli spaces in algebraic and symplectic geometry. The focus will be on moduli spaces appearing in the construction of invariants and structures in quantum geometry, such as Gromov-Witten and Donaldson-Thomas invariants, or various Floer-theoretic structures such as symplectic cohomology or some simple cases of the Fukaya category. The techniques will both be algebraic-geometric as well as analytic. This is an advanced class assuming some mastery of abstract algebraic geometry, as well as basics of algebraic and differential topology, and symplectic geometry. The format will alternate between traditional lectures and talks by participants on selected aspects.

M 393C (Tsai) Mathematics in Deep Learning

Prerequisites

For undergraduate students who want to enroll in this class, please talk to the instructor. The following qualification is highly recommended:
M341 or 340L with a grade of at least B.
M348, M368K with a grade of at least B.
M365C with a grade of at least B.

M 393C (Gualdani) Partial Differential Equations I

The topic of the graduate course will be on Nonlinear partial differential Equations, with emphases on elliptic and parabolic equations.

Second order Elliptic equations

• Existence of weak solutions
• Interior and exterior regularity
• Maximum principle
• Harnack’s inequality
• Cacciopoli’s inequality and applications
• Campanato’s spaces
• Schauder’s theory
• Hölder regularity estimates
• Calderon Zygmund decomposition

Second order Parabolic equations

• Existence of weak solutions
• Regularity
• Maximum principle
• Schauder’s estimates

Applications to kinetic equations

• Landau equation
• Landau-Fermi-Dirac equation

Bibliography

L.E. Evans, Partial Differential equations

E. H. Lieb and M. Loss, Analysis

G. M. Lieberman, Second order parabolic differential equations

O. A. Ladyženskaja, V. A. Solonnikov, and N. N. Ural’ceva, Linear and quasilinear equations of parabolic type

D. Gilbarg, N.S. Trudinger, Elliptic Partial Differential Equations of Second Order

M 393C (Chen) Methods in Mathematics Physics

The purpose of this graduate course is to provide an introduction to mathematical aspects of Quantum Mechanics and Quantum Field Theory, with connections and applications to neighboring research areas, which tentatively include kinetic equations and deep learning. No background in physics is required, but some knowledge of Analysis/PDE is useful.

M 394C (Zariphopoulou) Stochastic Processes I

The course offers a comprehensive study of Ito-diffusion processes, stochastic calculus and stochastic integration. It covers stochastic differential equations and their connection to classical analysis. It also exposes the students to optimal stochastic control of diffusion processes, the Hamilton-Jacobi-Bellman equation (classical and viscosity solutions), singular stochastic control and linear filtering. Applications, mainly from mathematical finance, inventory theory, decision analysis and insurance will be presented. If time permits, the course will offer a brief overview of multi-scale problems in stochastic analysis.

Topics

1. Review of fundamental concepts in probability
2. Martingales and filtrations
3. Brownian Motion
4. Stochastic Integration
5. Stochastic Calculus
6. Stochastic Differential Equations
7. Feynman-Kac formula and connection with linear PDE
8. Introduction to Optimal Stochastic Control
9. Introduction to Singular Stochastic Control
10. Filtering
11. Applications

1. Knowledge of Probability and fundamental concepts of Stochastic
2. Measure Theory and Real Analysis are highly
3. The level of the class will be mostly similar to the one in references 1-3

1. “An introduction to Stochastic Differential Equations”, L. C.
2. “Stochastic Differential Equations”, Oksendal (6th edition)
3. “Brownian Motion and Stochastic Calculus”, Karatzas and S. Shreve
4. “The theory of Stochastic Processes, I”, Gihman and A. Skorokhod
5. “The theory of Stochastic Processes, II, Gihman and A. Skorokhod
6. “Controlled Markov Processes and Viscosity Solutions”, H. Fleming and M. Soner

Some organizational issues about the course

• Class notes will be distributed via email. Other readings will be suggested and distributed later

• Homework will be assigned every week with strong expectation to be completed (and returned to me) within a

• Two take-home exams will be

• The grade will consist of the take-home exams (2x20%) and the HW assignments (60%).

M 3181D (Koch) Complex Analysis

Please visit the prelim courses syllabi

M 382D (Payne) Differential Topology

Please visit the prelim courses syllabi

M 383D (Gamba) Methods of Applied Mathematics

Please visit the prelim courses syllabi

M 385D (Sirbu) Theory of Probability

Please visit the prelim courses syllabi

M 387D (Arbogast) Numerical Analysis: Differential Equations

Please visit the prelim courses syllabi

M 392C (Dai) Floer Theory

The aim of this course is to serve as a (hopefully gentle) introduction to Heegaard Floer homology and its applications. We will begin by first discussing some of the types of problems that Floer theory has been used to answer, such as questions involving 3-manifolds, bordism, knots, and surgery. Our goal will be to describe the formal structure of Heegaard Floer and knot Floer homology (and explain how this structure is used to answer these questions), as well as give an overview of the construction of these theories.

M 392C (Wilkens) Geom Of Locally Compact Groups

This course will explore the behavior and characteristics of large (nondiscrete and noncompact) but nice (always locally compact, sometimes second countable) groups. We will use the book Metric geometry of locally compact groups by Yves Cornulier and Pierre de la Harpe, available on the arXiv, as a guide. In particular, we will view these groups as metric spaces, as well as probability spaces, and discuss properties and applications. We will start with an introduction via discrete groups and the Haar measure.

M 392C (Christ) Geometry of Algebraic Curves

M 393C (Delgadino) Partial Differential Equations II

Optimal transportation gradient flows with applications to mean field limits of parameter training dynamics:

In this course, we will review the recently developed theory of optimal transportation and gradient flows. We will emphasize the natural connections of this theory with probability and statistical mechanics. Specifically, the role of entropy as both a natural measure of uncertainty and as a rate functional for large deviations theory. As an application we will study the mean field limits of interacting particle systems, and the effect of phase transitions. To stay relevant, we will study the PDEs arising from parameter training in standard Machine Learning algorithms.

M 393C (Kileel) Foundational Techniques of Machine Learning/Data Science

This course will be a mathematically rigorous introduction to topics from linear algebra, high-dimensional probability, optimization and statistics, which are foundational tools for data science, or the science of making predictions from structured data.  A secondary aim of the course is for students to gain experience in exploring data science problems through computer programming.

M 393C (Seeger) Rough Paths/Stochastic PDEs

The course will begin with an introduction to the theory of rough paths, which is a general framework for understanding problems with very irregular time dependence. Such "roughness" often comes from wild stochastic oscillations, and the idea behind rough paths is to separate the analytical arguments (dealing with functions and distributions in spaces of low regularity) from the probabilistic. The rest of the course will cover certain PDEs with singular (often stochastic) forcing, where this same idea is explored. Some familiarity with stochastic calculus can be helpful, but it is not required, and this course is targeted toward graduate students working in analysis and/or PDE who are interested in learning what tools from analysis are used to study singular stochastic problems.

M 397C (Engquist) Mathematical Modeling

This graduate course in mathematical modeling for computational science and engineering (CSE) covers the modeling process and relevant properties of mathematical models. A variety of models will be discussed. There are no formal requirements, and the course is mainly self-contained. Some background in differential equations, scientific programming and probability will be useful. There will be a couple of minor projects during the semester and a more extensive one at the end but no final. The form of the projects will partially depend on the number of participants and on input from students. The material for the course will be from books, tutorials and papers the can be accessed on the internet as well as some lecture notes. The outline is given below:

(1) The mathematical modeling process

• Classical logic or science-based modeling

o Using science arguments to derive the mathematical formulations

• Data driven modeling

o From fully data driven models as, for example, machine learning to more science informed data driven models

• Model reduction: from one model to another with less complexity

o Classical asymptotic methods, averaging of dynamical systems, homogenization.

o CSE oriented model reduction, reduced order models, etc.

(2) Properties of mathematical models for CSE

• Wellposedness

o Existence, uniqueness, continuous dependence on data. The dependence on data includes classical norm-based estimates, condition numbers and in particular Uncertainty Quantification (UQ).

• Complexity

o Measures of model complexity: Information and complexity theory, Kolmogorov n-width.

o Analysis of related computational complexity. Related to model reduction above.

• Model data interaction

o Matching quality data to models

o Data assimilation, including remarks on digital twins.

M 381C (Bowen) Real Analysis

Please visit the prelim courses syllabi

M 382C (Danciger) Algebraic Topology

Please visit the prelim courses syllabi

M 383C (Arbogast) Methods of Applied Mathematics

Please visit the prelim courses syllabi

M 383E (Biros) Numerical Analysis: Linear Algebra

Description

Numerical linear algebra is fundamental in computing. From machine learning, optimization, and signal analysis, to mechanics, atomistic calculations and computational science, linear algebra is at the core of many higher-level algorithms. Understanding key numerical linear algebra methods and appreciating their complexity and limitations are invaluable skills for all scientists and engineers. The course covers matrix factorization algorithms like SVD, QR, and LU; Eigenvalue decompositions; and sparse iterative linear solvers.

M 384C Mathematical Statistics I

M 384G Regression Analysis

M 385C (Zitkovic) Theory of Probability

Description

M385C / CSE384K "Theory of Probability I" is the first part of a two-semester prelim graduate course in probability theory. Its aim is to develop a modern and mathematically rigorous theory of probability.

Topics covered

Foundations of measure theory: measurability and measures, Lebesgue integration, Lp-spaces, theorems of Fubini-Tonelli and Radon-Nikodym

Basic notions of probability: probability spaces, sigma-algebras and information, modes of convergence, characteristic functions, law(s) of large numbers, central limit theorems

Discrete-time martingales: conditional expectation, filtrations, martingales, convergence theorems, martingale inequalities, optional sampling theorems.

A set of lecture notes (written by the instructor) will be followed: go to https://bit.ly/3rjd6M9  (heading “Theory of Probability Parts I and II”, lectures 1 thru 13).

Prerequisites

Knowledge of multi-variable calculus (as in a typical calculus sequence), basic notions of elementary probability (as in Pittman’s “Probability”), real analysis (a thorough understanding at the level of first 7 chapters of Rudin’s “Principles of Real Analysis”) and linear algebra (as in Lay’s “Linear Algebra and its Applications”) are assumed. Some exposure to elementary stochastic processes (as in Ross’s “Introduction to Probability Models”) would be beneficial, but is not strictly required. All the measure theory needed will be developed, so measure theory is not a prerequisite.

M 387C (Enquist) Numerical Analysis: Algebra & Approx

Description

This is the first part of the Prelim sequence for Numerical Analysis, and it covers development and analysis of numerical algorithms for algebra and approximation. The second part covers differential equations. Below is an outline of topics for M387C.

Numerical solution of linear and nonlinear systems of equations including direct and iterative methods for linear problems, fixed point iteration and Newton type techniques for nonlinear systems. Eigenvalue and singular value problems.

Optimization algorithms: search techniques, gradient and Hessian based methods and constrained optimization techniques including Kuhn-Tucker theory.

Interpolation and approximation theory and algorithms including splines, orthogonal polynomials, FFT and wavelets.

Numerical integration and differentiation including Monte Carlo methods.

M 392C (Siebert) Complex Geometry

Complex geometry is the study of spaces endowed with holomorphic functions. The field connects the theory of complex functions in one variable with differential geometry (differentiable functions) and algebraic geometry (polynomial functions). Complex geometry indeed is an ample source of examples for differential geometry, while providing the geometric picture and intuition for algebraic geometry.

Topics: I) Local theory: holomorphic functions, differential forms, analytic sets.
II) Complex manifolds: Topology, examples, divisors, line bundles, coverings, blowing up.
III) Kähler manifolds: Kähler identities, Hodge theory, Lefschetz theorems.
IV) Special topic: According to interests of participants, most likely K3 surfaces.

M 392C (Bajaj) Geometric Foundations of Data Science

M 392C Knotted Surfaces

M 392C Lie Groups

M 392C (Perutz) Symplectic Topology

A symplectic structure on a smooth manifold is a non-degenerate, skew-symmetric pairing on each tangent space, i.e. a non-degenerate 2-form, satisfying what is essentially the only differential equation that is fully independent of coordinates: the condition that the 2-form is closed. Symplectic structures are rooted in mathematical physics, especially the Hamiltonian formulation of mechanics: a function on a symplectic manifold determines a vector field, whose flow describes the evolution of the system.

Locally, symplectic manifolds of any given dimension, necessarily even, all look alike. We will begin with basics of the subject, such as the proof of the latter assertion.

While symplectic geometry connects to many areas (like classical mechanics, quantization, algebraic geometry, mirror symmetry, representations of Lie groups, mircrolocal analysis, gauge theory), symplectic topology is concerned with the global structure of symplectic manifolds (existence, uniqueness, symmetries) and to fascinatingly intricate questions about the global structure compares to the differential topology of the underlying manifolds. In the latter part of this course, we will look closely at the most important tool of the symplectic topologist’s trade, the theory of pseudo-holomorphic curves: that is, how one studies symplectic manifolds by making their tangent bundles complex and then probing them via maps from Riemann surfaces with complex-linear derivative. I plan to include applications of this theory to automorphism groups of 4-dimensional symplectic manifolds, such as Gromov’s theorem that the symplectic automorphism group of the complex projective plane is homotopy-equivalent to its projective-linear subgroup PU(3), and to constraints on the existence of Lagrangian submanifolds.

Prerequisites: algebraic and differential topology at the level of the prelim sequence.

M 393C (Shriver) Entropy & Ergodic Theory

M 393C (Tsai) Mathematics In Deep Learning

M 393C (Taillefumier) Neural Dynamics & Information Theory

This course is intended for mathematicians interested in neuroscience and mathematically-inclined computational neuroscientists. The emphasis will be primarily on the analytical treatment of neuroscienceinspired models and algorithms. The objectives of the course is to equip students with a solid technical and conceptual background to tackle research questions in mathematical neuroscience. The course will be structured in three blocks: neural dynamics, information theory, and machine learning.

M 393C (Maggi) Partial Differential Equations I

M 393C (Bajaj) Predictive Machine Learning

M 393C (Patrizi) Viscosity Sol & Nonlinear Pdes

M 394C (Zariphopoulou) Stochastic Processes I

The course offers a comprehensive study of Ito-diffusion processes, stochastic calculus and stochastic integration. It covers stochastic differential equations and their connection to classical analysis. It also exposes the students to optimal stochastic control of diffusion processes, the Hamilton-Jacobi-Bellman equation (classical and viscosity solutions), singular stochastic control and linear filtering. Applications, mainly from mathematical finance, inventory theory, decision analysis and insurance will be presented. If time permits, the course will offer a brief overview of multi-scale problems in stochastic analysis.

Topics

1. Review of fundamental concepts in probability
2. Martingales and filtrations
3. Brownian Motion
4. Stochastic Integration
5. Stochastic Calculus
6. Stochastic Differential Equations
7. Feynman-Kac formula and connection with linear PDE
8. Introduction to Optimal Stochastic Control
9. Introduction to Singular Stochastic Control
10. Filtering
11. Applications

1. Knowledge of Probability and fundamental concepts of Stochastic
2. Measure Theory and Real Analysis are highly
3. The level of the class will be mostly similar to the one in references 1-3

1. “An introduction to Stochastic Differential Equations”, L. C.
2. “Stochastic Differential Equations”, Oksendal (6th edition)
3. “Brownian Motion and Stochastic Calculus”, Karatzas and S. Shreve
4. “The theory of Stochastic Processes, I”, Gihman and A. Skorokhod
5. “The theory of Stochastic Processes, II, Gihman and A. Skorokhod
6. “Controlled Markov Processes and Viscosity Solutions”, H. Fleming and M. Soner

Some organizational issues about the course

• Class notes will be distributed via email. Other readings will be suggested and distributed later

• Homework will be assigned every week with strong expectation to be completed (and returned to me) within a

• Two take-home exams will be

• The grade will consist of the take-home exams (2x20%) and the HW assignments (60%).

M 380D (Keel) Algebra II

Please visit the prelim courses syllabi

M 381D (Vasseur) Complex Analysis

Please visit the prelim courses syllabi

M 382D (Allcock) Differential Topology

Please visit the prelim courses syllabi

M 383D (Arbogast) Methods of Applied Mathematics

Please visit the prelim courses syllabi

M 385D (Zitkovic) Theory of Probability

Please visit the prelim courses syllabi

M 387D (Engquist) Numerical Analysis: Differential Equations

Please visit the prelim courses syllabi

M 392C (Damiolini) Conformal Blocks, Generalized Theta Functions, and the Verlinde Formula

M 392C (Freed) Geometric Topics in Field Theory

Description

In this lecture course I survey the geometric structure of quantum theory—primarily quantum field theory—and its interaction with mathematics. The varied mathematics brought in will be reviewed/introduced, but not necessarily developed in full detail.  This includes relevant topics from analysis, algebra, geometry, and topology.  The course is aimed at those interested in engaging with mathematical ideas inspired by quantum field theory and/or with the physics directly.

I begin with quantum mechanics, move on to traditional axiomatizations of quantum field theory, and then the bulk of the course is in the context of modern geometric axiom systems for Wick-rotated quantum field theories. Topological field theories are included, but they are not the primary focus. Quantum gravity will not be discussed, nor will we do much with perturbation theory.

Some mathematical applications to be discussed may include: invariants of knots and low-dimensional manifolds, symplectic topology, (higher) representation theory, and index theory.

M 392C (Ben-Zvi) Literature Seminar in Geometry

Description

Students in this class will read and give talks about classic papers in geometry. The focus is much more on the process of tackling a paper and presenting mathematics effectively than it is about the specific material in the papers. This class is modeled on the famous "Kan seminar" at MIT. One major difference: the Kan seminar deals exclusively with algebraic topology whereas in this class the papers encompass the entire spectrum of geometry: geometric analysis, differential geometry, algebraic geometry, topology, representation theory etc.

I will compile a list of papers in advance of the class. Each student will choose, study, and present to the class three of these papers. The first paper will be short with a single well-defined result. Subsequent papers will have more extended structure and scope. As the semester progresses and the level of the papers rises, a major challenge is to extract a mathematical "story" from the papers and to then express the authors' ideas and vision. I will work with each student before and after the talks. Students in the class will also work with each other to practice talks and discuss the material; they will also do evaluations of each talk in the class.

Enrollment is limited to eight graduate students. Students should be roughly on the cusp of doing thesis research. That is, they should be essentially done with prelim exams but not more than a year or so past the qualifying exam. I expect most students to be in the second or third year.

Students need not be working in geometry to participate in this class.
If you are interested in this class, you must come talk to me about it.
For more information see the course web page from a previous time it was taught by Dan Freed:

http://www.ma.utexas.edu/users/dafr/M397S/index.html

M 393C (Tsai) Deep Learning II

• Optimal control, Hamilton-Jacobi equation and viscosity solutions, Pontryagin Maximum Principle
• Approximation theory for feedforward neural networks, including the theory about Baron space, structural preservation networks, and neural tangent kernels
• Multiscale aspects on the optimization models. We will discuss modern computational multiscale methods and related theory in numerical analysis
• Mathematical theory on gradient flows, including the Jordan-Kinderlehrer-Otto schemes
• Sampling algorithms, including some variants of Markov Chain Monte Carlo methods
• Deep learning approaches for scientific computing

M 393C (Kileel) Fundamental Technique Machine Learning and Data Science

Description

This course will be a mathematically rigorous introduction to topics from linear algebra, high-dimensional probability, optimization, statistics, which are foundational tools for data science, or the science of making predictions from structured data. A secondary aim of the course is to become comfortable with experimenting and exploring data science problems through programming.

M 393C (Maggi) Partial Differential Equations II

Description

This PDE II course will focus on the minimal surfaces equation both as a chief example of elliptic nonlinear PDE, and in its relation to the theory of smooth minimal surfaces. The course will include a careful review of the computational tools from Riemannian Geometry needed to work with minimal surfaces. If time permits, some aspects of the parabolic version of the minimal surfaces problem (mean curvature flow) will be discussed.

M 393C (Gamba) Statistical Mechanics of Kinetic Interactive Systems in Mean Field Modeling

Description

This topics course covers broad issues on collisional theory arising from particle systems. We will discuss two class of systems:  the first one consists are Boltzmann and Landau sets equations for conservative and conservative systems and their connections to non-equilibrium statistical mechanics. The second one are related to weak turbulence models in plasma physic,  arising from perturbation of collisionless Vlasov-Poisson and Vlasov-Maxwell around bulk statistical equilibria resulting in a model reduction form coupling diffusion equations for the electron particle couple to spectral wave density from the Poisson or Maxwell equation in Fourier representation.

• Introduction and elementary properties.  Binary elastic interaction, time irreversibility, conservation laws, H-theorem and energy inequalities for inelastic interactions.    The grazing collision limit for Coulomb interactions and the connection to the Landau Equation.  Small mean free path, Hilbert and Chapman expansions. Moment methods and connections to hydrodynamic models in fluid dynamics. 
• Space homogeneous problems under special averaging regimes.

Collision systems: Angular averaging lemmas and gain of integrability properties for hard potentials. Existence and uniqueness properties in connection of moment inequalities. Carleman integral representation and comparisons principles for pointwise bounds to solutions. Summability of moments and exponential moments and tails. Solutions to the Cauchy problem by ODE in Banach spaces.  Convolution inequalities for collision operators. Fourier representation of the Boltzmann and relativistic Landau equations. W^m,p(R^d)-theory, p in [1, ∞].   Special case of kinetic equations of Maxwell type, special solutions in Fourier space.

Mean field coupled systems:  emerging particle-spectral wake energy systems in the modeling weak turbulence in plasma dynamics by small perturbation of bulk equilibrium states, under dynamics for null special averages.  Braking of symmetry and absence of Landau damping mechanisms. The quasilinear spectral wave system and  Balescu-Lenard models.

• Numerical approximations to kinetic particle systems. Deterministic solvers for linear and non-linear collisional forms of Boltzmann and Landau type. Conservative spectral and FEM methods. Galerkin-Petrov schemes and moment methods.  Comparisons to Discrete Simulation Monte Carlo (DSMC) methods. Stability and error estimates. Applications to kinetic models for plasmas and charge transport as well as to inverse problem in nano-scale. The Boltzmann-Poisson system.        

We will have an extra hour discussion time when needed on a date and place to be set.

The following is a suggested bibliography:

• Cercignani, C.; The Boltzmann Equation and its Applications, Springer, New York, 1988.
• Cercignani, C., Illner, R. and Pulvirenti, M.; The Mathematical Theory of Diluted Gases", Springer, NY,1994.
• Villani, C.; A review of Mathematical topics in collisional kinetic theory, Handbook of Fluid Mechanics, 2003.
• Nicholson, D.R.; Introduction to Plasma Theory. John Wiley and Sons, 1982.
• Sone, Y.; Kinetic Theory and Fluid Dynamics (Birkhäuser, 2002): Click here to download supplementary notes and errata
• Sone, Y., Molecular Gas Dynamics (Birkhäuser, 2007): Click here to download supplementary notes and errata
• Stix, T.H.; Waves in Plasmas, AIP, NY, 1992.
• Thorne, K.S. and Blandford, R.D,; Modern Classical Physics: Optics, Fluids, Plasmas, Elasticity, Relativity, and Statistical Physics, Princeton University Press, 2017.
• Class notes and several recent papers to be distributed in class.

Prerequisites: Some knowledge of Methods of Applied Analysis and Mathematics and Partial Differential Equations is beneficial

Testing and examination plan and policies: Attendance at lectures is expected. A 30-min prepared presentation on a topic to be discussed with the instructor.  

There will be neither exams nor tests for this course.

Evaluation:   The course and instructor will be evaluated at the end of the semester using the approved form. 

This course maybe viewed as complementary to CSE 397 / EM 397

The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Office of the Dean of Students at 471-6259, 471-4641 TTY.

M 393C (Caffarelli) Fundamental Partial Differential and Non-Local Equations, linear and non-linear classic theory and recent developments

Textbook: Several relevant publications will be made available, as well as suggested reading from textbooks.

Description

Some of the topics to be considered are:

• Non local diffusion equations, in particular involving a fractional Laplacian.
• Optimal transport.
• Harnack inequality and regularity for solutions of second order elliptic equations with bounded measurable coefficients: De Giorgi, Nash, Moser theorem for divergence form equations; and Krylov Safonov for non-divergence forms.
• Notion of viscosity solutions treatment of elliptic equations, in particular regularity of fully non linear equations by Crandall-Lions and Evans and its connections to Perron method by super and sub solutions.
• Applications to the Obstacle and Free Boundary problems.

Evaluation:  A written or oral presentation. No final exam is required.

Prerequisite: Some basic knowledge of graduate Real Analysis, first graduate PDEs, and some issues discussed in the classical graduate Applied Mathematics Methods Prelim course, is strongly recommended.

M 394C (Sirbu) Topics in Stochastic Analysis

Description

Topics in Stochastic Analysis consists of a selection of topics roughly following up to the Theory of Probability I and II and is independent of other Topics courses in the field. 
The emphasis is on Stochastic Differential Equations (SDEs) and optimization problems involving such equations (stochastic control problems). An important amount of time will be dedicated to a special class of optimization problems, namely Optimal Stopping. For Optimal Stopping Problems, we can present a rather complete and rigorous probabilistic treatment (with some comments on the relation to the corresponding analytic theory) and some (mostly explicit) examples. While some of these examples may be related to Finance, the emphasis will be on the mathematical part. Other important topics in Stochastic Control will be covered, time permitting. The course assumes some understanding of discrete-time probability (Theory of Probability I) and of the Stochastic Integration Theory (Theory of Probability II). 

M 380C (Ciperiani) Algebra I

Please visit the prelim courses syllabi

M 381C (Bowen) Real Analysis

Please visit the prelim courses syllabi

M 382C (Allcock) Algebraic Topology

Please visit the prelim courses syllabi

M 383C (Koch) Methods of Applied Mathematics

Description

An introductory course in linear Functional Analysis.

After some Preliminaries (integration, various spaces, properties, examples) we will cover the basics on Banach spaces (continuous linear functionals and transformations; Hahn-Banach extension theorem; duality, weak convergence; Baire theorem, uniform boundedness; Open Mapping, Closed Graph, and Closed Range theorems; compactness; spectrum, Fredholm alternative), Hilbert spaces (orthogonality, bases, projections; Bessel and Parseval relations; Riesz representation theorem; spectral theory for compact, self-adjoint and normal operators; Sturm-Liouville theory), and Distributions (seminorms and locally convex spaces; test functions, distributions; calculus with distributions; etc.) with examples and applications. These are roughly the topics listed on the Applied Math. Course Syllabus

Textbook 

None

Some References

E. Kreyszig, Introductory Functional Analysis with Applications, John Wiley & Sons, 1978.

J.B. Conway, A Course in Functional Analysis, Springer, 1990.

W. Rudin, Functional Analysis, McGraw-Hill, 1973.

M. Reed, B. Simon, Functional Analysis, Academic Press, 1980.

R. Meise, D. Vogt, Introduction to Functional Analysis, Oxford University Press, 1997.

K. Yosida, Functional Analysis, Springer, 1980.

H.L. Royden, Real Analysis, MacMillan, 1988.

• Lang, Real Analysis, Addison Wesley,
• Arbogast, Bona, Methods of Applied Mathematics, Course Notes, 2005.

Prerequisites

Knowledge of the subjects taught in the undergraduate analysis course M365C and an undergraduate course in linear algebra. 

M 385C (Zitkovic) Theory of Probability

Description

M385C / CSE384K "Theory of Probability I" is the first part of a two-semester prelim graduate course in probability theory. Its aim is to develop a modern and mathematically rigorous theory of probability.

Topics covered

Foundations of measure theory: measurability and measures, Lebesgue integration, Lp-spaces, theorems of Fubini-Tonelli and Radon-Nikodym

Basic notions of probability: probability spaces, sigma-algebras and information, modes of convergence, characteristic functions, law(s) of large numbers, central limit theorems

Discrete-time martingales: conditional expectation, filtrations, martingales, convergence theorems, martingale inequalities, optional sampling theorems.

A set of lecture notes (written by the instructor) will be followed: go to https://bit.ly/3rjd6M9  (heading “Theory of Probability Parts I and II”, lectures 1 thru 13).

Prerequisites

Knowledge of multi-variable calculus (as in a typical calculus sequence), basic notions of elementary probability (as in Pittman’s “Probability”), real analysis (a thorough understanding at the level of first 7 chapters of Rudin’s “Principles of Real Analysis”) and linear algebra (as in Lay’s “Linear Algebra and its Applications”) are assumed. Some exposure to elementary stochastic processes (as in Ross’s “Introduction to Probability Models”) would be beneficial, but is not strictly required. All the measure theory needed will be developed, so measure theory is not a prerequisite.

M 387C (Engquist) Numerical Analysis: Algebra & Approx

Please visit the prelim courses syllabi

M 392C (Perutz) Gauge Theory and 4-manifold Topology

Smooth manifolds of dimension 4 do not play by the rules that govern smooth manifolds in higher dimensions. Those rules say, roughly, that smooth simply connected manifolds, of dimension at least 5, are governed (up to a finite indeterminacy) by their homotopy-type together with the isomorphism class of the tangent bundle as a vector bundle. In dimension 4, this picture is quite wrong, as we know through gauge theory. The classification of simply connected, smooth 4-manifolds is the outstanding mystery of geometric topology, but the facts revealed by gauge theory are extraordinary.

The application of gauge theory to 4-manifold topology began in the early 1980s with the geometric analysis of instantons. Its scope expanded in the mid-90s with the introduction of the Seiberg-Witten equations.  This course will focus on the the Seiberg-Witten equations, and will include proofs of some of the classic theorems of the subject, such as the diagonalizability of negative-definite intersection forms of 4-manifolds, and the genus-minimizing property of symplectic surfaces in a symplectic 4-manifold.  The methods involve differential geometry and geometric analysis as well as some algebraic topology. Detailed notes, from an earlier incarnation of this course, will be made available.

I expect you to be familiar with manifolds and homology at the level of the two Topology prelims, and to be comfortable with undergraduate-level analysis.  While experience with differential geometry is advantageous, differential-geometric concepts such as connections and spinors will be explained.

M 392C (Burungale) Intro to Arithmetic Of Elliptic Curves

M 392C (Payne) Moduli Spaces

This course will cover relations between tropical geometry and geometry of algebraic moduli spaces.  We will discuss dual complexes of normal crossing divisors and the skeleton associated to a normal crossing compactification, with the Deligne-Mumford stable curves compactification of the moduli space of smooth curves as a motivating example.  We will show that the skeleton of the Deligne-Mumford compactification is naturally identified with a moduli space of stable tropical curves.  As time permits, we will discuss applications of this identification to the cohomology of moduli spaces of curves.

M 392C (Danciger) Geometric Structures on Manifolds

The first half of the course will cover the basics of geometric structures and their general deformation theory, and a rapid introduction to/review of hyperbolic geometry.  Then we will explore some non-Riemannian geometric structures that nonetheless have close connections with hyperbolic geometry. Specifically, this semester I plan to focus on convex real projective structures, mainly in dimension two, but also three and higher. Draw a convex set in the plane. What is its automorphism group? That depends, of course, on what you mean by automorphism. We take automorphism to mean projective linear automorphism (fractional linear map). For a large family of special convex sets, the automorphism group is a surface group, whose action on the interior of the convex set is properly discontinuous. The quotient by the action is a convex real projective surface. The geometry and deformation theory of these surfaces (and their higher dimensional analogues) is rich and beautiful, with many interesting avenues to explore. 

M 392C (Siebert) Tropical Geometry

Description

Tropical geometry is the study of piecewise linear structures appearing in the study of degenerations of algebraic or rigid-analytic objects. Famous examples are plane tropical curves, given by an embedded graph in the real plane with line segments having rational slopes. These objects have found striking applications in enumerative geometry, the study of moduli spaces, mirror symmetry, real algebraic geometry, combinatorics, the study of cluster algebras, and even in optimization, data analysis and phylogenetics.

The course gives an introduction to the subject emphasizing many different points of views. No previous knowledge of algebraic geometry is assumed. There will be room to highlight some applications depending on the interests of the participants.

M 393C (Tsai) Deep Learning I

M 393C (Gualdani) Partial Differential Equations I

M 393C (Vasseur) Intro to Conservation Laws

Course overview

The course will focus on the study of models for compressible fluid mechanics as the compressible Navier-Stokes equation or the compressible Euler equation. the course will begin with fundamental properties of general conservation laws, including the development of discontinuous pattern in finite time. A large part of the lecture will be dedicated to the stability of these discontinuous patterns, especially in the invsicid limit.

Requirement

This course expects students to have a strong background in Real analysis (equivalent to Graduate Real Analysis) and Functional analysis (Graduate Applied Math I and II) and to have some basic knowledge of Partial differential equations (Graduate PDE I).

M 394C (Zariphopoulou) Stochastic Processes I

The course offers a comprehensive study of Ito-diffusion processes, stochastic calculus and stochastic integration. It covers stochastic differential equations and their connection to classical analysis. It also exposes the students to optimal stochastic control of diffusion processes, the Hamilton-Jacobi-Bellman equation (classical and viscosity solutions), singular stochastic control and linear filtering. Applications, mainly from mathematical finance, inventory theory, decision analysis and insurance will be presented. If time permits, the course will offer a brief overview of multi-scale problems in stochastic analysis.

Topics

1. Review of fundamental concepts in probability
2. Martingales and filtrations
3. Brownian Motion
4. Stochastic Integration
5. Stochastic Calculus
6. Stochastic Differential Equations
7. Feynman-Kac formula and connection with linear PDE
8. Introduction to Optimal Stochastic Control
9. Introduction to Singular Stochastic Control
10. Filtering
11. Applications

1. Knowledge of Probability and fundamental concepts of Stochastic
2. Measure Theory and Real Analysis are highly
3. The level of the class will be mostly similar to the one in references 1-3

1. “An introduction to Stochastic Differential Equations”, L. C.
2. “Stochastic Differential Equations”, Oksendal (6th edition)
3. “Brownian Motion and Stochastic Calculus”, Karatzas and S. Shreve
4. “The theory of Stochastic Processes, I”, Gihman and A. Skorokhod
5. “The theory of Stochastic Processes, II, Gihman and A. Skorokhod
6. “Controlled Markov Processes and Viscosity Solutions”, H. Fleming and M. Soner

Some organizational issues about the course

• Class notes will be distributed via email. Other readings will be suggested and distributed later

• Homework will be assigned every week with strong expectation to be completed (and returned to me) within a

• Two take-home exams will be

• The grade will consist of the take-home exams (2x20%) and the HW assignments (60%).

M 391C (Beckner) Fourier Analysis

Our lectures will survey the classical structure and techniques of Fourier Analysis – starting from the development of the Lebesgue integral and the framework of the Riemann-Lebesgue lemma, the Plancherel theorem, Young’s inequality, the Hausdorff-Young inequality, and the uncertainty principle. The drive for “best constants” for intrinsic inequalities that characterize smoothness and encode geometric information was initiated by Hardy & Littlewood. The essential role of symmetry emerges with the isoperimetric and Brunn-Minkowski inequalities, the Riesz-Sobolev rearrangement theorem and its application for Sobolev inequalities. Topics that will be discussed include: (1) distribution theory, the Schwartz class, spherical harmonics, and the Hecke-Bochner representation, (2) restriction, Bochner-Riesz means, and Strichartz inequalities, (3) Kunze-Stein phenomena on Lie groups, (4) Gaussian functions and log Sobolev

inequalities, (5) Riesz potentials, the fractional Laplacian, Stein-Weiss integrals, the Hardy-Littlewood-Sobolev inequality, and Moser- Trudinger inequalities, (6) embedding estimates on the sphere and hyperbolic space, and (7) analysis on the Heisenberg group using both conformal and symplectic geometry in moving forward beyond the Euclidean domain.

M 391C (Lafleche) Semiclassical Dynamics

This course will present some of the mathematical tools to describe the links between quantum and classical theories. Semiclassical analysis aims to understand asymptotic expansions in terms of a small parameter often corresponding to the Planck constant. Such expansions however usually require a certain regularity that must be proved for dynamical models.

The main topics of the lectures will be

- the basis of the underlying physical theories

- the Wigner and Husimi transforms, the Weyl and Wick quantizations

- operator theory, Schatten spaces, trace and semiclassical inequalities

- the quantum Wasserstein distances and Sobolev spaces

- the large particle approximations and the limit from the Hartree to the Vlasov equation

M 392C (Siebert) Algebraic Geometry

This is a second course in algebraic geometry, assuming some knowledge of scheme theory as contained e.g. in Chapter 2 of Hartshorne's classic book on the subject. We first introduce cohomological methods and then, as an application of the learned machinery, study the moduli space of stable curves as an algebraic stack. Further topics will be added if time permits.

M 392C (Stecker) Discrete Subgroups of Lie Groups

A Lie group G, such as the group of invertible nxn matrices, carries both the structure of a group and that of a manifold, in particular it comes with a topology. So we can ask which subgroups of G are discrete in this topology. These subgroups play an important role in geometry. For example, (finitely generated torsion-free) discrete subgroups of the group PSL(2,R) are exactly the holonomy groups of hyperbolic surfaces, and so understanding them is essentially equivalent to understanding the geometry of hyperbolic surfaces.

A relatively new field often called "Higher Teichmüller Theory" aims to study discrete subgroups of higher rank semisimple Lie groups, for example PSL(n,R), by similar geometric methods. This is often done by focusing on classes of discrete subgroups with particularly nice properties. Examples are Hitchin representations, maximal representations, divisible convex sets, or the more general Anosov representations.

The plan for this course is to give an introduction into these tools and explore some of their fascinating geometric and dynamical properties. We will start with the discrete subgroups of rank one groups like PSL(2,R) and some of the hyperbolic geometry contained in them, and then build up to the definition of Anosov representations into PSL(n,R). After that,
further topics could for example be: the characterization of Anosov representations via singular values, other Lie groups, Hitchin representations, maximal representations, positivity, convex projective manifolds, the limit cone of Zariski dense groups.

Prerequisites: it would be good to know basic differential topology (smooth manifolds, Lie groups), differential geometry (the hyperbolic

M 392C (Bajaj) Geometric Foundations Data Science/Predictive Machine Learning

Discuss foundational mathematical, statistical and computational theory of data sciences. Explore how this data driven predictive machine learning theory is applied to stochastic dynamical systems, optimal control and multi-player games.

M 392C (Chen) The Gromov norm and bounded cohomology

The aim of the course is to introduce the relatively new and fast-growing field of study on Gromov's norm and bounded cohomology, their variants, and most importantly applications to more classical topics (mostly in geometry, topology and dynamics). The hope is that the course will provide useful tools and points of views for students studying (hyperbolic) geometry, (low-dimensional) topology and dynamics.

Mostow's rigidity asserts that hyperbolic structure is unique (if exists) on closed manifolds of dimension at least 3. In particular, the hyperbolic volume is (surprisingly) a topological invariant (in all dimensions). As a wonderful explanation of this fact, Gromov introduced the simplicial volume that measures the topological complexity of the fundamental class and showed that it is proportional to the hyperbolic volume if the manifold is hyperbolic.

The simplicial volume is a special case of Gromov's (simplicial) norm, which equips each homology group (of a given space with R coefficients) a semi-norm that measures the complexity of each homology class. For the second homology group of a 3-manifold, this turns out to be proportional to the Thurston norm, which reveals how the manifold fibers over the circle.

Bounded cohomology is the dual theory of simplicial norm (similar to the L^\infty-L^1 duality). The two theories complement each other and have numerous applications to geometry, topology and dynamics (see below), especially in the understanding of groups that arise naturally in these fields. We will also discuss variants of the simplicial norm and their corresponding dual theory.

We will cover a subset of the following topics: Simplicial volume and Gromov's proportionality, a proof of Mostow's rigidity; Thurston's norm and fibrations of 3-manifolds, relation between Thurston's norm and Gromov's norm; bounded cohomology, quasimorphisms, applications to transformation groups; examples and constructions of quasimorphisms on free groups, hyperbolic groups, and mapping class groups; groups acting on the circle, Poincare's rotation number, and the bounded Euler class, relation to left- and circular-orderability; stable commutator length, applications to the existence of surface (and free) subgroups of certain groups acting on trees and/or random groups; Burger-Monod's vanishing theorem for bounded cohomology of higher rank irreducible lattices and its application to rigidity of such lattices; the spectrum of stable commutator length and the spectrum of simplicial volume, spectral gap properties.

Prerequisites: The students are expected to be familiar with basic notions from algebraic topology (fundamental groups, free groups, Euler characteristic, covering spaces). Having some ideas about hyperbolic geometry would be helpful for certain topics of the course, but it is not required. Otherwise, prerequisites will be kept to a minimum.

M 392C (Perutz) A Second Course in Algebraic Topology

This course will build on the foundation provided by the Algebraic Topology prelim course, and will cover some of the central ideas of the subject, concerning homotopy theory and cohomology. This is material that is widely used in differential and algebraic geometry, geometric topology, and algebra, as well as by specialists in algebraic topology.  The following is an aspirational list of topics, of which I hope to cover several:

Homological algebra: Examples of derived functors: Tor, Ext, group (co)homology

Singular homology: Review of singular and cellular homology, Eilenberg-Steenrod axioms. Homology of products. Cohomology and universal coefficients. Simplicial spaces; construction of classifying spaces for topological groups. 

Cup products and duality:  Cross, cup and cap products. Manifolds, orientation and Poincaré duality. Submanifolds and transverse intersections. 

Homotopy theory:  Homotopy groups; fiber bundles and fibrations. Cofibrations. Weak equivalence and Whitehead’s theorems. The homotopy exact sequence of a fibration. The Hurewicz theorem. Eilenberg-MacLane spaces.

The Serre spectral sequence: The spectral sequence of a filtered complex. The Serre spectral sequence; examples; transgression. Proof of the Hurewicz theorem. 

Localization:  Serre classes of abelian groups; homotopy and homology theory modulo a Serre class; applications.

Prerequisite: Algebraic Topology at the level of the prelim: fundamental groups, covering spaces, basics of homology theory (e.g. Mayer-Vietoris).  You should also know the basics of rings and modules, as in the Algebra I prelim - for instance, the tensor product of modules.

Texts:  I plan to draw on Haynes Miller’s MIT lecture notes in Algebraic Topology (https://math.mit.edu/~hrm/papers/lectures-905-906.pdf), as well texts including those of Allen Hatcher (http://pi.math.cornell.edu/~hatcher/AT/ATpage.html) and Peter May (https://www.math.uchicago.edu/~may/CONCISE/ConciseRevised.pdf).

M 393C (Ward) Foundational Techniques of Machine Learning and Data Sciences

This course will be a mathematically rigorous introduction to topics from linear algebra, high-dimensional probability, optimization, statistics, which are foundational tools for data science, or the science of making predictions from structured data. A secondary aim of the course is to become comfortable with experimenting and exploring data science problems through programming.

M 393C (Vasseur) Partial Differential Equations II

Mathematical treatment of fluid mechanics

This course is an introduction to the mathematical study of partial differential equations applied to fluid mechanics. We will consider both compressible and incompressible models, and study the properties of their solutions. A special focus will be given to the questions of well-posedness, stability, and regularity.

M 394C (Zitkovic) Volatility Modeling

Volatility is a local measure of variability of the price of a financial asset. It plays a central role in modern finance, not only because it is the main ingredient in the celebrated Black-Scholes option-pricing formula. One of its most enticing aspects is that it is as interesting to mathematicians and statisticians as it is to financial practitioners. As the markets, and our understanding of them, evolve and as our statistical prowess grows, the models we use to describe volatility become more and more sophisticated. 

The goal of this course is to give an overview of various models of volatility, together with their most important mathematical aspects. In addition, these models provide a perfect excuse to talk about various classes of stochastic processes (Gaussian processes, affine
diffusions or rough processes).

While the main focus will remain on the underlying mathematics, some time will be spent on statistical properties of these models and their fit to data. 

Thorough familiarity with measure-theoretic probability and basic concepts of stochastic analysis (at the level of M385C/M385D "Theory of Probability I/II", respectively) will be assumed. No prior knowledge of finance or statistics will be required. 

M 380C (Raskin) Algebra

The prelim sequence is M380C and M380D

Description

It is assumed that students know the basic material from an undergraduate course in linear algebra and an undergraduate abstract algebra course. The first part of the Prelim examination will cover sections 1 and 2 below. The second part of the Prelim examination will deal with section 3 below.

1. Groups:     Finite groups, including Sylow theorems, p-groups, direct products and sums, semi-direct products, permutation groups, simple groups, finite Abelian groups; infinite groups, including normal and composition series, solvable and nilpotent groups, Jordan-Holder theorem, free groups. 
  
References:  Goldhaber  Ehrlich, Ch. I except 14; Hungerford, Ch. I, II; Rotman, Ch. I-VI, VII (first three sections).

2. Rings and modules:     Unique factorization domains, principal ideal domains, modules over principal ideal domains (including finitely generated Abelian groups), canonical forms of matrices (including Jordan form and rational canonical form), free and projective modules, tensor products, exact sequences, Wedderburn-Artin theorem, Noetherian rings, Hilbert basis theorem. 
  
References:     Goldhaber  Ehrlich, Ch. II, III  1,2,4, IV, VII, VIII;  Hungerford, Ch. III except 4,6, IV 1,2,3,5,6, VIII 1,4,6.

3.  Fields:     Algebraic and transcendental extensions, separable extensions, Galois theory of finite extensions, finite fields, cyclotomic fields, solvability by radicals. 
  
References:     Goldhaber  Ehrlich, Ch. V except 6;  Hungerford, Ch. V, VI;  Kaplansky, Part I.

References: 
        Goldhaber  Ehrlich, Algebra, reprint with corrections, Krieger, 1980. 
        Hungerford, Algebra, reprint with corrections, Springer, 1989. 
        Isaacs, Algebra, a Graduate Course, Wadsworth, 1994. 
        Kaplansky, Fields and Rings, 2nd Edition, University of Chicago Press, 1972. 
        Rotman, An Introduction to the Theory of Groups, 4th Edition, W.C. Brown, 1995. 

M 381C (Caffarelli) Real Analysis

Description

The objective of this syllabus is to aid students in attaining a broad understanding of analysis techniques that are the basic stepping stones to contemporary research. The prelim exam normally consists of eight to ten problems, and the topics listed below should provide useful guidelines and strategy for their solution. It is assumed that students are familiar with the subject matter of the undergraduate analysis courses M365C and M361. The first part of the Prelim examination will cover Real Analysis. The second part of the prelim examination will cover Complex Analysis.

1. Measure Theory and the Lebesgue Integral
   Basic properties of Lebesgue measure and the Lebesgue integral on Rⁿ(see [5], Ch. 1-4) and general measure and integration theory in an abstract measure space (see [5], Ch. 11-12; and especially [6], Ch. 1-2). L^p spaces (see [6], Ch. 3); convergence almost everywhere, in norm and measure; approximation in L^p-norm and L^p-L^q duality; integration in product spaces (see [6], Ch. 8) and convolution on Rⁿ; and the concept of a Banach space, Hilbert space, dual space and the Riesz representation theorem. 
2. Holomorphic Functions and Contour Integration
   Basic properties of analytic functions of one complex variable (see [1], Ch. 4-5; [2], Ch. 4-7; [4], Ch. 4-8; or [6], Ch. 10-12 and 15). Integration over paths, the local and global forms of Cauchy's Theorem, winding number and residue theorem, harmonic functions, Schwarz's Lemma and the Maximum Modulus theorem, isolated singularities, entire and meromorphic functions, Laurent series, infinite products, Weierstrass factorization, conformal mapping, Riemann mapping theorem, analytic continuation, "little" Picard theorem. 
3. Differentiation
   The relationship between differentiation and the Lebesgue integral on a real interval (see [5], Ch. 5), derivatives of measures (see [6], Ch. 5), absolutely continuous functions and absolute continuity between measures, functions of bounded variation. 
4. Specific Important Theorems
   Students should be familiar with Monotone and Dominated Convergence theorems, Fatou's lemma, Egorov's theorem, Lusin's theorem, Radon-Nikodym theorem, Fubini-Tonelli theorems about product measures and integration on product spaces, Cauchy's theorem and integral formulas, Maximum Modulus theorem, Rouche's theorem, Residue theorem, and Fundamental Theorem of Calculus for Lebesgue Integrals. Students should be familiar with Minkowski's Inequality, Holder's Inequality, Jensen's Inequality, and Bessel's Inequality. 

References
1. L. Ahlfors, Complex Analysis, McGraw-Hill, New York, 1979. 
2. J.B. Conway, Functions of One complex Variable, second edition, Springer-Verlag, New York, 1978. 
3. G.B. Folland, Real Analysis, second edition, John Wiley, New York, 1999. 
4. B. Palka, An Introduction to Complex Function Theory, second printing, Springer-Verlag, New York, 1995. 
5. H.L. Royden, Real Analysis, Macmillan, New York, 1988. 
6. W. Rudin, Real and Complex Analysis, third edition, McGraw-Hill, New York, 1987. 
7. R. Wheeden and A. Zygmund, Measure and Integral, Marcel Dekker, New York, 1977. 

M 382C (Gordon) Algebraic Topology

Description

MANIFOLDS AND CELL COMPLEXES:
Identification (quotient) spaces and maps;
Topological n-manifolds including surfaces, S^n, RP^n, CP^n;
CW decompositions, including these examples.

FUNDAMENTAL GROUP AND COVERING SPACES:
Fundamental group, the first example S^1;
Functoriality and homotopy-type invariance;
Retraction and deformation retraction;
Van Kampen's Theorem;
Covering spaces and lifting properties;
Covering transformations;
The covering spaces <--> subgroups of pi_1 correspondence;
Regular covers;
Further examples including RP^n and lens spaces.

SINGULAR HOMOLOGY:
Definitions, functoriality, homotopy-type invariance;
Relative homology, excision, the Eilenberg-Steenrod axioms;
Mayer-Vietoris and examples, including S^n, CP^n;
Homology of cell complexes;
Further examples, including RP^n;
Local homology and orientations, degree of a map : S^n \to S^n;
Brouwer fixed point theorem, Jordan separation theorem;
Euler characteristic, Lefschetz fixed point theorem.

PRINCIPAL TEXT:
Hatcher, Algebraic Topology (available for free download)

The syllabus is approximately Chapters 0-2 of Hatcher.

OLDER REFERENCES (all 35+ years old but still useful):
Armstrong, Basic Topology, Springer
Greenberg, Lectures on Algebraic Topology
Massey, Algebraic Topology, an Introduction
Munkres, Elements of Algebraic Topology

M 383C (Arbogast) Methods of Applied Mathematics

Description

It is assumed that students are familiar with the subject matter of the undergraduate analysis course M365C (see the Analysis section for a syllabus of that course) and an undergraduate course in linear algebra.

The Applied Math Prelim divides into these eight areas. The first four are discussed in M383C and will be covered in the first part of the Prelim examination:

Banach spaces: Normed linear spaces, convexity, and examples; convergence, completeness, and Banach spaces; continuity, open sets, and closed sets; bounded linear transformations; Hahn-Banach Extension Theorem and its applications; the Baire Theorem and uniform boundedness; Open Mapping and Closed Graph Theorems; linear functionals, dual and reflexive spaces, and weak convergence.

Hilbert spaces: Inner products, basic geometry, orthogonality, bases, projections, and examples; the Riesz Representation Theorem; Bessel’s inequality and the Parseval Theorem; maximal orthonormal sets and the Riesz-Fischer Theorem; weak convergence.

Spectral theory: Resolvent and spectrum; compact operators; spectral theory for compact, self-adjoint, and normal operators; the Fredholm alternative; the Ascoli-Arzelà Theorem; Sturm-Liouville Theory.

Distributions: Seminorms and locally convex spaces; test functions and distributions; operations with distributions; approximations to the identity; applications to linear differential operators.

The following four areas are discussed in M383D and will be covered in the second part of the Prelim examination:

The Fourier transform: The Schwartz space and tempered distributions; the Fourier transform on L-2 and tempered distributions; the Plancherel Theorem; convolutions; fundamental solutions of PDE’s.

Sobolev spaces: Definitions and basic properties; extensions theorems; the Sobolev Embedding Theorem; compactness and the Rellich-Kondrachov Theorem; fractional order spaces and trace theorems.

Variational Boundary Value Problems (BVP): Weak solutions to elliptic BVP’s; variational forms and satisfaction of Dirichlet and Neumann boundary conditions; Closed Range Theorem; Lax-Milgram Theorem; Galerkin methods; Green’s functions.

Differential Calculus in Banach spaces and Calculus of Variations: The Fréchet derivative; the Chain Rule and Mean Value Theorems; Banach’s Contraction Mapping Theorem and Newton’s Method; Inverse and Implicit Function Theorems, and applications to nonlinear functional equations; extremum problems and the Euler-Lagrange equation; problems with constraints and Lagrange multipliers.

References

R. A. Adams, Sobolev Spaces, Academic Press, 1975.
T. Arbogast and J. L. Bona, Functional Analysis for the Applied Mathematician, 2020.
A. Avez, Introduction to Functional Analysis, Banach Spaces, and Differential Calculus, Wiley, 1986.
C. Carath'eodory, Calculus of Variations and Partial Differential Equations of the First Order, 2nd English Edition, Chelsea, 1982.
Ph. Ciarlet, Linear and Nonlinear Functional Analysis with Applications, SIAM, 2013.
L. Debnath and P. Mikusi'nski, Introduction to Hilbert Spaces with Applications, Academic Press, 1990.
I.M. Gelfand and S.V. Fomin, Calculus of Variations, Prentice-Hall, 1963.
E. Kreyszig, Introductory Functional Analysis with Applications,1978.
J.T. Oden and L.F. Demkowicz, Applied Functional Analysis, CRC Press, 1996.
M. Reed and B. Simon, Methods of Modern Physics, Vol. 1, Functional analysis.
W. Rudin, Functional Analysis, McGraw-Hill, 1991.
W. Rudin, Real and Complex Analysis,3rd Edition, McGraw-Hill, 1987.
K. Yosida, Functional Analysis, Springer-Verlag, 1980.

M 383E (Martinsson) Numerical Analysis: Linear Algebra

Description

Matrix computations form the core of much of scientific computing, and are omnipresent in applications such as statistics, data mining and machine learning, economics, and many more. This first year graduate course focuses on some of the fundamental computations that occur in these applications. Specific topics include direct and iterative methods for solving linear systems, standard factorizations of matrices (LU, QR, SVD), and techniques for solving least squares problems. We will also learn about basic principles of numerical computations, including perturbation theory and condition numbers, effects of roundoff error on algorithms and analysis of the speed of algorithms.

Pre-requisites for this course are a solid knowledge of undergraduate linear algebra, some familiarity with numerical analysis, and prior experience with writing mathematical proofs.

M 384C Mathematical Statistics I

Description

The two semesters of this course (M 384C and M 384D) are designed to provide a solid theoretical foundation in mathematical statistics.

During the TWO-SEMESTER course, the statistical topics include the properties of a random sample, principles of data reduction (sufficiency principle, likelihood principle, and the invariance principle), and theoretical results relevant to point estimation, interval estimation, hypothesis testing with some work on asymptotic results.   

During the first semester, M384C, students are expected to use their knowledge of an undergraduate upper-level probability course and extend those ideas in enough depth to support the theory of statistics, including some work in hierarchical models to support working with Bayesian statistics in the second semester. Students are expected to be able to apply basic statistical techniques of estimation and hypothesis testing and also to derive some of those techniques using methods typically covered in an undergraduate upper-level mathematical statistics course. A brief review of some of those topics is included. Probability methods are used to derive the usual sampling distributions (min, max, the t and F distributions, the Central Limit Theorem, etc.) Methods of data reduction are also discussed, particularly through sufficient statistics. This includes the five chapters of the text and part of the sixth chapter as well as some additional material on estimation and hypothesis testing.

Prerequisite:
M362K, Probability, and M378K, Introduction to Mathematical Statistics, or the equivalent. Course descriptions of 362K and 378K are available on the web and more information about equivalencies is available from http://www.ma.utexas.edu/users/parker/384/prereq/

Textbook:  Statistical Inference by George Casella and Roger L. Berger, second edition

Consent of Instructor Required:  Yes.

M 384G Regression Analysis

M 385C (Neeman) Theory of Probability

Prerequisites:

• Real Analysis (M365C or equivalent),
• Linear Algebra (M341 or equivalent),
• Probability (M362K or equivalent).
• R. Durrett, Probability: theory and examples, fifth ed., Duxbury Press, Belmont, CA, 1996. (required)
• D. Williams, Probability with martingales, Cambridge University Press, Cambridge, 1991. (recommended)

Literature

5th edition of Durrett's book. A free pdf of this book is available at Durrett's website (https://services.math.duke.edu/~rtd/PTE/PTE5_011119.pdf)

Syllabus: (Note: all references are to Durrett's book) 

Description

Foundations of Probability:

• Random variables (Sections 1.1, 1.2, 1.3): probability spaces, σ-algebras, measurability, continuity of probabilities, product spaces, random variables, distribution functions, Lebesgue-Stieltjes measures (without proof), random vectors, generation, a.s.-convergence
• Expected value (Section 1.4, 1.5, 1.6): abstract Lebesgue integration (without proofs), inequalities (Jensen, Cauchy-Schwarz, Chebyshev, Markov, Hölder, Minkowski), limit theorems (Fatou's lemma, monotone convergence and dominated convergence theorems), change-of-variables formula,
• Dependence (Section 2.1): independence, pairwise independence, Dynkin's - theorem, convolution of measures, Fubini's theorem, Kolmogorov's extension theorem (without proof)

Classical Theorems:

• Weak laws of large numbers (Sections 2.2, 2.3, 2.4): the L² -weak law of large numbers, triangular arrays, Borel-Cantelli lemmas, modes of convergence, inequalities (Markov, Chebyshev, Jensen, Hölder), the weak law of large numbers
• Central limit theorems (Sections 3.1, 3.2, 3.3, 3.4): weak convergence of distributions, the continuous mapping theorem, Helly's selection theorem, tightness, characteristic functions, the inversion theorem, continuity theorem, the central limit theorem, multivariate normal distributions

Discrete-Time Martingale Theory:

• Conditional expectation (Sections 4.1.1, 4.1.2): Radon-Nikodym theorem (without proof), conditional expectation, filtrations, predictability and adaptivity
• Martingales (Sections 4.2, 4.4, 4.5, 4.6, 4.7): martingale transforms, the optional sampling theorem, the upcrossing inequality, Doob's decomposition, Doob's inequality, Lp -convergence, maximum inequalities, L² -theory, uniform integrability, backwards martingales and the strong law of large numbers.

M 387C (Engquist) Numerical Analysis: Algebra & Approximations

Description

This is the first part of the Prelim sequence for Numerical Analysis, and it covers development and analysis of numerical algorithms for algebra and approximation. The second part covers differential equations. Below is an outline of topics for M387C.

Numerical solution of linear and nonlinear systems of equations including direct and iterative methods for linear problems, fixed point iteration and Newton type techniques for nonlinear systems. Eigenvalue and singular value problems. 

Optimization algorithms: search techniques, gradient and Hessian based methods and constrained optimization techniques including Kuhn-Tucker theory.

Interpolation and approximation theory and algorithms including splines, orthogonal polynomials, FFT and wavelets.

Numerical integration and differentiation including Monte Carlo methods. 

M 392C (Ben-Zvi) Algebraic Geometry

Description

This will be a first course in modern algebraic geometry, largely following the textbook by Ravi Vakil, The Rising Sea: Foundations of Algebraic Geometry. Some familiarity with basics of category theory and commutative algebra recommended. 

M 391C (Radziwill) Introduction to Analytic Number Theory

Description

This course will be an introduction to analytic number theory. We will focus on multiplicative and additive aspects. As far as multiplicative number theory is concerned we will cover the prime number theorem, the Bombieri-Vinogradov theorem, properties of the Riemann zeta-function and L-functions, sieve theory and the method of bilinear forms. We will also cover some of the main tools of additive number theory: namely the circle method and methods for bounding exponential sums and see how these tools are applied in practice, for instance to proving Birch's theorem or studying rational points lying close to curves. While we will cover the basics I will also emphasize the modern directions of the field; e.g how the circle method can be used to count points on varieties (e.g Birch's theorem, Manin's conjectures) and how the knowledge of the analytic properties of L-functions is relevant to anybody interested in automorphic forms or the broader Langlands program.

The anticipated start date is September 14. Please contact Prof. Radziwill at

maksym.radziwill@gmail.com

M 392C (Freed) Differential Geometry

Description

Differential geometry is the application of calculus to geometry on smooth manifolds.  Felix Klein's Erlangen program defines geometry in terms of symmetry, and in the first part of the course we delve into its manifestation in smooth geometry.  So we begin with basics about Lie groups and move on to the geometry of connections on principal bundles.  We focus in particular on the bundle of frames and geometric structures on manifolds.

Armed with this general theory, we can move in many directions.  Possible topics include Chern-Weil theory of characteristic classes; topics in Riemannian geometry, symplectic geometry, and spin geometry; differential equations on manifolds; curvature and topology.  Students' interest will influence the particular topics covered.

Prerequisites: Familiarity with smooth manifolds and calculus on smooth manifolds at least at the level of the prelim class.

M 392C (Kileel) Geometric Methods in Data Science 

Description

This is a graduate topics course on geometric methods in data science.  Data sets in applications often have interesting geometry.  For example, individual data points might consist of images or volumes.  Alternatively, the totality of the data may be well-approximated by a low-dimensional space.   This course surveys computational tools that exploit geometric structure in data, as well as some of the underlying mathematics.

The syllabus will adapt to the interests of course participants, but we plan to survey some of the following topics:

• principal component analysis (including streaming PCA), 
• tensor decomposition and latent variable estimation (higher-order PCA), 
• clustering methods (k-means and mixture of Gaussians), 
• non-linear dimensionality reduction (manifold learning), 
• numerical optimization (semi-definite programming and possibly semi-algebraic optimization or manifold optimization).

Time permitting, we might look at applications of group theory in data science.

We will be reading excerpts from important papers and monographs.  Students will present some fraction of the lectures (with coaching from the instructor), write up lecture notes, and submit a final project with a written report.  For the project, students may choose between applying methods to real data sets or writing a synopsis of a theoretical paper.  For real data sets, possible sources include signal processing, microscopy or computer vision applications, among others.  

The course's main prerequisites are linear algebra, basic probability, and mathematical maturity.  Programming familiarity (or willingness to learn) will help with certain projects.  A few elements of differential and algebraic geometry will be developed along the way. 

M 392C (Bowen) Hyperbolic Geometry and Teichmüller Theory

Description

The aim of this course is to give students a working knowledge of hyperbolic geometry and Teichmuller spaces. Topics may include:  Hausdorff dimension of limit sets, Patterson-Sullivan theory, geodesic flows, quasi-conformal maps, quadratic differentials, flat surfaces, measured laminations, Weil-Peterson symplectic form, pseudo-Anosov maps, mapping class groups, Teichmuller flows, quasi-Fuchsian surfaces, hyperbolic 3-manifolds that fiber of the circle, Surface Subgroup Theorem, Agol’s virtual fibering Theorem.

Prerequisites: Students are expected to be familiar with measure theory, L^p spaces and smooth manifolds. Otherwise, prerequisites will be kept to a minimum. 

References

Marden, Albert Hyperbolic manifolds. An introduction in 2 and 3 dimensions. Cambridge University Press, Cambridge, 2016. xviii+515 pp. ISBN: 978-1-107-11674-0

Casson, Andrew J.; Bleiler, Steven A. Automorphisms of surfaces after Nielsen and Thurston. London Mathematical Society Student Texts, 9. Cambridge University Press, Cambridge, 1988. iv+105 pp. ISBN: 0-521-34203-1

Farb, Benson; Margalit, Dan A primer on mapping class groups. Princeton Mathematical Series, 49. Princeton University Press, Princeton, NJ, 2012. xiv+472 pp. ISBN: 978-0-691-14794-9

Gardiner, Frederick P.; Lakic, Nikola Quasiconformal Teichmüller theory. Mathematical Surveys and Monographs, 76. American Mathematical Society, Providence, RI, 2000. xx+372 pp. ISBN: 0-8218-1983-6 

Matheus Silva Santos, Carlos Dynamical aspects of Teichmüller theory.

SL(2,ℝ)-action on moduli spaces of flat surfaces. Atlantis Studies in Dynamical Systems, 7. Atlantis Press, [Paris]; Springer, Cham, 2018. xiv+122 pp. ISBN: 978-3-319-92158-7; 978-3-319-92159-4

Kapovich, Michael Hyperbolic manifolds and discrete groups. Reprint of the 2001 edition. Modern Birkhäuser Classics. Birkhäuser Boston, Ltd., Boston, MA, 2009. xxviii+467 pp. ISBN: 978-0-8176-4912-8

M 392C (Allcock) Lie Groups

Description
Material: We will cover the basic theory of Lie Groups and Lie algebras, from the Lie correspondence through the classification theorem (over the complex numbers) and highest weight modules. If there is time, then I would also like to talk about Kazhdan's property (T), and/or the structure theory of real Lie groups and algebras. This structure theory tells you how to think about real Lie groups, but we will not be able to cover their classification. This theory is a prerequisite to understanding infinite-dimensional representations of Lie groups, but we will not be able to cover any of that, either.

Prerequisites: I will assume you are comfortable with the material from our first year graduate courses in topology and algebra. From topology, you absolutely need fluency with the fundamental group, covering spaces, and the language of differentiable manifolds. We will use deRham coholomogy a little bit, but you could get by with just a high-level understanding of it. From algebra you need fluency with group theory and multilinear algebra (including bilinear and Hermitian forms, and tensor products). No Galois theory will be needed, only a tiny bit of commutative algebra, and nothing with a ground field other than the real or complex numbers. From analysis we need only a little: enough to understand statements about Haar measure, and maybe what a Banach space is.

Textbook: notes prepared by me.

M 392C (Gualdani) PDE I

The topic of the graduate course will be on Nonlinear partial differential Equations, with emphases on elliptic and parabolic equations.

• Existence of weak solutions
• Interior and exterior regularity
• Maximum principle
• Harnack’s inequality
• Cacciopoli’s inequality and applications
• Campanato’s spaces
• Schauder’s theory
• Hölder regularity estimates
• Calderon Zygmund decomposition

• Existence of weak solutions
• Regularity
• Maximum principle
• Schauder’s estimates

• Landau equation
• Landau-Fermi-Dirac equation

L.E. Evans, Partial Differential equations

E. H. Lieb and M. Loss, Analysis

G. M. Lieberman, Second order parabolic differential equations 

O. A. Ladyženskaja, V. A. Solonnikov, and N. N. Ural’ceva, Linear and quasilinear equations of parabolic type 

D. Gilbarg, N.S. Trudinger, Elliptic Partial Differential Equations of Second Order

M 393C (Maggi) Minimal Surfaces from the viewpoint of Geometric Measure Theory

Abstract: The course addresses the study of minimal surfaces from the viewpoint of Geometric Measure Theory. A basic goal is creating a theory of non-smooth surfaces which is flexible enough to contain limits of sequences of minimal surfaces under natural geometric bounds. Another direction is using the compactness theorems so obtained to apply variational methods to the study of minimal surfaces, for example, in proving the existence of minimal surfaces satisfying certain sets of constraints. Finally, we shall address the regularity problem for the generalized minimal surfaces created in the process.

M 393C (Tsai) Mathematics in Deep Learning

Prerequisites

For undergraduate students who want to enroll in this class, please talk to the instructor. The following qualification is highly recommended:
M341 or 340L with a grade of at least B.
M348, M368K with a grade of at least B.
M365C with a grade of at least B.

M 394C (Zariphopoulou) Stochastic Processes I

Description

The course offers a comprehensive study of Ito-diffusion processes, stochastic calculus and stochastic integration. It covers stochastic differential equations and their connection to classical analysis. It also exposes the students to optimal stochastic control of diffusion processes, the Hamilton-Jacobi-Bellman equation (classical and viscosity solutions), singular stochastic control and linear filtering. Applications, mainly from mathematical finance, inventory theory, decision analysis and insurance will be presented. If time permits, the course will offer a brief overview of multi-scale problems in stochastic analysis.

Topics

1. Review of fundamental concepts in probability
2. Martingales and filtrations
3. Brownian Motion
4. Stochastic Integration
5. Stochastic Calculus
6. Stochastic Differential Equations
7. Feynman-Kac formula and connection with linear PDE
8. Introduction to Optimal Stochastic Control
9. Introduction to Singular Stochastic Control
10. Filtering
11. Applications

Background

1. Knowledge of Probability and fundamental concepts of Stochastic Processes.
2. Measure Theory and Real Analysis are highly recommended.
3. The level of the class will be mostly similar to the one in references 1-3 below.

Suggested readings besides the class notes

1.  “An introduction to Stochastic Differential Equations”, L. C. Evans.
2. “Stochastic Differential Equations”, B. Oksendal (6^th edition)
3. “Brownian Motion and Stochastic Calculus”, I. Karatzas and S. Shreve
4. “The theory of Stochastic Processes, I”, I. Gihman and A. Skorokhod
5. “The theory of Stochastic Processes, II, I. Gihman and A. Skorokhod
6. “Controlled Markov Processes and Viscosity Solutions”, W.H. Fleming and M. Soner

Some organizational issues about the course:

• Class notes will be distributed via email. Other readings will be suggested and distributed later on.

• Homework will be assigned every week with strong expectation to be completed (and returned to me) within a week.

• Two take-home exams will be given.

• The grade will consist of the take-home exams (2x20%) and the HW assignments (60%).