Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization. With the exception of the Foundations and Data Models course, the requirements can be met with different courses in different schools.
Mathematical Foundations of Machine Learning
This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, ECE, and ISyE.
- CS/CSE/ECE/ISYE 7750, Mathematical Foundations of Machine Learning
Probabilistic and Statistical Methods in Machine Learning
- ISYE 6412, Theoretical Statistics
- ECE/ISYE/CS/CSE 7751, Probabilistic Graphical Models
- MATH 7241, High Dimensional Probability
- MATH 7242 High Dimensional Statistics
Machine Learning Theory and Methods
This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning. Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two.
- CS 7545 Machine Learning Theory and Methods
- CS 7616, Pattern Recognition
- CSE/ISYE 6740, Computational Data Analysis
- ECE 6254, Statistical Machine Learning
- ECE 6273, Methods of Pattern Recognition with Applications to Voice
Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance. The five courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.
- ECE 6270, Convex Optimization: Theory, Algorithms, and Applications
- ISYE 6661, Linear Optimization
- ISYE 6663, Nonlinear Optimization
- ISYE 7683, Advanced Nonlinear Programming