New Core Courses
This page contains material on the core curriculum of the Graduate Interdisciplinary Program (GIDP) in Applied Mathematics at the University of Arizona (upgraded in 2019-2020).
Core classes of the Applied Mathematics GIDP are
- Math 527 a,b: Theory -- Principles of Analysis (Theoretical Foundations of Applied Mathematics).
- Instructor: Shankar Venkataramani.
- Math 583 a,b: Methods -- Principles and Methods in Applied Mathematics.
- Instructor: Misha Chertkov.
- Math 575 a,b: Algorithms -- Numerical Analysis (Numerical Analysis and Algorithms of Applied Mathematics).
- Instructor: Misha Stepanov.
# in UA catalog a=fall,b=spring: Short Name -- Official Name (Tentative Future Name)
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 |
|---|---|---|---|---|
| 527:Theory | Analysis | Integration | Optimization (Theory) |
Statistics & Probability |
| 583: Methods | Applied Analysis | Differential Equations | Optimization (Methods) |
Applied Probability & Statistics |
| 575: Algorithms | Numerical Algebra & Analysis |
Numerical Differential Equations |
Optimization (Algorithms) |
Inference & Learning |
The following books are recommended as 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
