The graduate curriculum in the Department includes permanent courses which are offered every year or every other year, as well as special topics courses which the faculty offer on topics of current interest. Special topics courses are designed by the instructor to lead graduate students to deeper study of a particular area that might lead to thesis research. For the regularly scheduled courses, see this page. The lists below give special topics courses which were offered in the last few years. Statistics courses are listed separately from mathematics courses.
In general, courses numbered 600-699 are basic graduate courses preparing students to take the basic part of the qualifying exams, while 700-799 are more advanced courses. Courses numbered 500-599 are open to graduate students and advanced undergraduate students, and are most often taken by students in the Applied Math Masters and Statistics Master’s programs. The exact topics covered by each of these classes may vary from year to year.
Courses were held at the UMass Amherst Campus unless otherwise noted.
Detailed course descriptions can be found on SPIRE.
Recent Mathematics Courses
| Semester | Instructor | Course Title |
|---|---|---|
| Spring 2025 | Markos Katsoulakis | Mathematics of Generative Modeling |
| Spring 2025 | Martina Rovelli | Category theory, model category theory, and applications |
| Fall 2024 | Yao Li | Real and Artificial Neural Networks |
| Fall 2024 | Inanc Baykur | Topological Data Analysis |
| Fall 2024 | Siman Wong | Zeta functions of algebraic varieties |
| Spring 2024 | Paul Gunnells | Generating Functions |
| Spring 2024 | Andrea Nahmod | Probabilistic Methods in Nonlinear Dispersive PDEs |
| Fall 2023 | Leili Shahriyari | Biomedical and Health Data Analysis |
| Fall 2023 | Andreas Buttenschoen | Mathematical (Cell) Biology |
| Fall 2023 | Owen Gwilliam | Characteristic classes and K-theory |
| Spring 2023 | Yulong Lu, Wei Zhu | Mathematical Theory of Machine Learning II |
| Spring 2023 | Annie Rayomond | Combinatorial Optimization |
| Spring 2023 | Eyal Markman | Abelian Varieties |
| Fall 2022 | Yulong Lu, Wei Zhu | Mathematical Theory of Machine Learning |
| Fall 2022 | Luc Rey-Bellet | Information Theory and Optimal Transport |
| Spring 2022 | Mark Wilson | Analytic Combinatorics |
| Spring 2022 | Inanc Baykur | Low Dimensional Topology |
| Fall 2021 | Owen Gwilliam | Homological Algebra |
| Fall 2021 | Hongkun Zhang | Networks and Spectral Graph Theory |
| Spring 2021 | Luc Rey-Bellet, Markos Katsoulakis | Math. Foundations of Probabilistic AI II |
| Spring 2021 | Owen Gwilliam | Moduli Spaces in Reprsentation Theory |
| Fall 2020 | Franz Pedit | Introduction to Riemann Surfaces |
| Fall 2020 | Luc Rey-Bellet, Markos Katsoulakis | Math. Foundations of Probabilistic AI |
| Spring 2020 | Annie Raymond | Sums of Squares |
| Spring 2020 | Paul Gunnells | Topology & Geometry of Singular Spaces |
| Spring 2020 | Franz Pedit | Infinite Dimensional Integral Systems |
| Fall 2019 | Alejandro Morales | Symmetric functions and representation theory of the symmetric group |
| Spring 2019 | Annie Raymond | Combinatorial Optimization |
| Spring 2019 | Paul Hacking | Algebraic Surfaces |
| Spring 2019 | Matthew Dobson | Calculus of Variations |
| Fall 2018 | Alejandro Morales | Convex Polytopes |
| Fall 2018 | Hongkun Zhang | Dynamical Systems and Ergodic Theory |
| Spring 2018 | Panos Kevrekidis | Nonlinear Waves & Applications in Continua and Lattices |
| Spring 2018 | Jenia Tevelev | Derived Categories |
Recent Statistics Courses
| Semester | Course Number | Course Title |
|---|---|---|
| Spring 2023 | Stat 608 (offered at Amherst and Mount Ida campus) | Mathematical Statistics II |
| Spring 2023 | Stat 610 (Mount Ida campus) | Bayesian Statistics |
| Spring 2023 | Stat 690STA* | Applied Semiparametric Regression |
| Spring 2023 | Stat 697MV* (Mount Ida campus) | Applied Multivariate Statistics |
| Spring 2023 | Stat 697V* (Mount Ida campus) | Data Visualization |
| Fall 2022 | Stat 607 | Mathematical Statistics I (Offered at Amherst and Mt. Ida campus) |
| Fall 2022 | Stat 625 | Regression Modeling (Offered at Amherst and Mt. Ida campus) |
| Fall 2022 | Stat 691P | Project Seminar (Newton-Mt. Ida campus) |
| Fall 2022 | Stat 697BD* | Biomed and Health Data Analysis |
| Fall 2022 | Stat 697L* | Categorical Data Analysis (Newton-Mt. Ida campus) |
| Fall 2022 | Stat 697SC* | Statistical Consulting: Bringing theory to practice |
| Fall 2022 | Stat 725 | Eastmtn Theory and Hypothesis Testing I |
| Spring 2022 | Stat 608 (offered at both Amherst and Mt. Ida campuses) | Mathematical Statistics II |
| Spring 2022 | Stat 697DS (Newton-Mt. Ida) | Statistical Methods for Data Science |
| Spring 2022 | Stat 697MV* (Newton-Mt.Ida) | Applied Multivariate Statistics |
| Spring 2022 | Stat 697TS* | Time Series Analysis |
| Spring 2022 | Stat 697V* (Newton-Mt.Ida) | Data Visualization |
| Fall 2021 | Stat 607 (offered at both Amherst and Mt. Ida campuses) | Mathematical Statistics I |
| Fall 2021 | Stat 610 | Bayesian Statistics |
| Fall 2021 | Stat 625 (offered at both Amherst and Mt. Ida campuses) | Regression Modeling |
| Fall 2021 | Stat 691P (Newton-Mt. Ida campus) | S-Project Seminar |
| Fall 2021 | Stat 697L (Newton-Mt. Ida campus) | ST-Categorical Data Analysis |
| Fall 2021 | Stat 697ML | ST-Stat Machine Learning |
| Fall 2021 | Stat 697TS (Newton-Mt. Ida campus) | ST-Time Series Analysis and Appl |
| Fall 2021 | Stat 705 | Linear Models I |
| Fall 2021 | Stat 797S | ST-Estimation/Semi Non Parametric Models |
| Spring 2021 | Stat 608 (offered at Amherst and Mount Ida campuses) | Mathematical Statistics II |
| Spring 2021 | Stat 697D* | Applied Statistics and Data Analysis |
| Spring 2021 | Stat 697DS* (Mount Ida campus) | Statistical Methods for Data Science |
| Spring 2021 | Stat 697MV* (Mount Ida campus) | Applied Multivariate Statistics |
| Spring 2021 | Stat 697V* (Mount Ida campus) | Data Visualization |
| Fall 2020 | Stat 607 (offered at Amherst and Mount Ida campuses) | Mathematical Statistics I |
| Fall 2020 | Stat 610 | Bayesian Statistics |
| Fall 2020 | Stat 625 (offered at Amherst and Mount Ida campuses) | Regression Modeling |
| Fall 2020 | Stat 691P | S-Project Seminar |
| Fall 2020 | Stat 697BD* | Biomedical and Health Data Analysis |
| Fall 2020 | Stat 697L* (Mount Ida campus) | Categorical Data Analysis |
| Fall 2020 | Stat 697ML* | Statistical Machine Learning |
| Fall 2020 | Stat 697TS* (Mount Ida campus) | Time Series Analysis and Application |
| Fall 2020 | Stat 725 | Estimation Theory and Hypothesis Testing I |
| Spring 2020 | Stat 526 (Mount Ida Campus) | Design Of Experiments |
| Spring 2020 | Stat 598C | Statistical Consulting Practicum |
| Spring 2020 | Stat 608 (offered at Amherst and Mount Ida Campuses) | Mathematical Statistics ll |
| Spring 2020 | Stat 691P | Project Seminar |
| Spring 2020 | Stat 697DS* (Mount Ida Campus) | Statistical Methods/Data Science |
| Spring 2020 | Stat 697L* | Categorical Data Analysis |
| Spring 2020 | Stat 697TS* | Time Series Analysis and Appl |
| Spring 2020 | Stat 797S* | Estimation/SemiNonParametMD |
| Fall 2019 | Stat 535 (offered at Amherst and Mount Ida Campuses) | Statistical Computing |
| Fall 2019 | Stat 598C | Statistical Consulting Practicum |
| Fall 2019 | Stat 605 (now Math 605) | Probability Theory l |
| Fall 2019 | Stat 607 (offered at Amherst and Mount Ida Campuses) | Mathematical Statistics l |
| Fall 2019 | Stat 610 | Bayesian Statistics |
| Fall 2019 | Stat 625 (offered at Amherst and Mount Ida Campuses) | Regression Modeling |
| Fall 2019 | Stat 697BD* | Biomedical and Health Data Analysis |
| Fall 2019 | Stat 697ML* | Statistical Machine Learning |
| Fall 2019 | Stat 705 | Linear Models 1 |
| Spring 2019 | Stat 597G* | Intro to Statistical Learning |
| Spring 2019 | Stat 598C | Statistical Consulting Practicum |
| Spring 2019 | Stat 608 | Mathematical Statistics ll |
| Spring 2019 | Stat 691P | Project Seminar |
| Spring 2019 | Stat 697D* | Applied Statistics & Data Analysis |
| Fall 2018 | Stat 535 | Statistical Computing |
| Fall 2018 | Stat 598C | Statistical Consulting Practicum |
| Fall 2018 | Stat 605 (now Math 605) | Probability Theory l |
| Fall 2018 | Stat 607 | Mathematical Statistics l |
| Fall 2018 | Stat 625 | Regression Modeling |
| Fall 2018 | Stat 697S* | Statistical Network Inference |
| Fall 2018 | Stat 725 | Estimation Theory & Hypothesis Testing |
| Fall 2018 | Stat 797N* | Non-parameteric Regression for Data Analysis |
| Spring 2018 | Stat 526 | Design of Experiments |
| Spring 2018 | Stat 598C | Statistical Consulting Practicum |
| Spring 2018 | Stat 608 | Mathematical Statistics ll |
| Spring 2018 | Stat 691P | Project Seminar |
| Spring 2018 | Stat 797L* | Mixture Models |
| Fall 2017 | Stat 535 | Statistical Computing |
| Fall 2017 | Stat 597L* | Dynamic Linear Models |