This page contains material on the core curriculum of the Graduate Interdisciplinary Program (GIDP) in Applied Mathematics at the University of Arizona:
Effective Fall 2021, the Core classes of the Applied Mathematics GIDP are:
- Math 584 A/B Theoretical Foundations of Applied Mathematics
- Instructor: Chris Henderson (2022-23); Shankar Venkataramani (2021-2022)
- Math 581 A/B Methods in Applied Mathematics
- Instructor: Laura Miller (581A, 2022); Misha Chertkov (581A/B, 2021-2022; 581B, 2023)
- Math 589 A/B Numerical Analysis and Algorithms of Applied Mathematics
- Instructors: Leonid Kunyansky (589A, 2022); Misha Stepanov (589A, 2021-2022; 589B, 2023)
All the classes are mandatory for the Applied Mathematics (AM) first year students (major in AM).
See sub-pages highlighted within the table below for description (syllabus) of the core classes curriculum and review of Introductory Material.
| CLASS NAME | EARLY FALL | LATE FALL | EARLY SPRING | LATE SPRING |
|---|---|---|---|---|
| 584:Theory | Analysis | Integration | Optimization (Theory) |
Statistics & Probability |
| 581: Methods | Applied Analysis | Differential Equations | Optimization (Methods) |
Applied Probability & Statistics |
| 589: Algorithms | Numerical Algebra & Analysis |
Numerical Differential Equations |
Optimization (Algorithms) |
Inference & Learning |
The Core Course Notes and additional learning resources as well as Introductory Material, including overview of the core courses, review of topics, guide to incoming students and sample problems.
Please check background reading recommendations to help prepare for the core classes.
- Analysis
- Applied Analysis
- Applied Probability & Statistics
- Background reading
- Differential Equations
- Inference & Learning
- Integration
- Numerical Algebra & Analysis
- Numerical Differential Equations
- Optimization (Algorithms)
- Optimization (Methods)
- Optimization (Theory)
- Books Recommended
- Statistics & Probability