Courses

Students can become affiliated with Machine Learning @ UW by joining one of any number of affiliated  programs. While Machine Learning @ UW is not a department/degree program and does not directly accept graduate students, faculty in multiple departments and degree programs are affiliated with this group (see People). Students interested in pursuing graduate studies relevant to machine learning research areas should select from the departments and degree programs affiliated with our faculty and apply for graduate admissions.

Selecting a department/program

The choice of department is important, as it will determine the faculty mentors that are available to advise you and the coursework you will fulfill. Prospective students should examine the list of Machine Learning @ UW faculty to determine which departments/degree programs align best with the faculty they are interested in working with and their curricular interests/background. If there is more than one department/degree program that is of interest, you may apply to up to 3 UW-Madison programs with a single application fee. To help admissions committees in these departments/degree programs identify prospective students, the Machine Learning @ UW encourages you to clearly state your research interests in the statement of purpose and note your interest in being part of the Machine Learning @ UW. We also strongly recommend that you contact potential graduate advisors by e-mail to discuss your research interests prior to application review.

Opportunities for funding

Many Machine Learning @ UW faculty are part of interdisciplinary training programs that offer pre-doctoral student trainee-ships. Students are encouraged to discuss these opportunities with their potential graduate advisors during the application process.

Coursework

wdt_ID Department Title
2 STAT 453: Introduction To Deep Learning And Generative Models
3 CS 760: Machine Learning
7 CS, ECE, ME 532: Matrix Methods in Machine Learning
8 Math 535: Mathematical Methods in Data Science
9 CS 540: Intro to Artificial Intelligence
10 CS 639: Undergraduate Elective Topics in Computing
11 Psychology 711: Introduction to Applied Machine Learning
13 CS 839: Verified Deep Learning
14 Political Science 919: Machine Learning
15 BMI, CS 576: Intro Bioinformatics
16 CS, ECE, ISYE 524: Introduction to Optimization
17 CS, ECE 533: Image Processing
18 CS 545: Natural Language and the Computer
19 ECE 729: Information Theory
20 CS, ECE 761: Mathematical Foundations of Machine Learning
21 CS 769: Advanced Natural Language Processing
22 STAT 451: Introduction to Machine Learning and Statistical Pattern Classification
23 CS, ECE, ME 539: Introduction to Artificial Neural Network and Fuzzy Systems
24 STAT 860: Estimation of Functions from Data
25 STAT 679: Computing Tools for Data Analytics
26 Physics 361: Machine Learning in Physics
27 GEN BUS 656: Machine Learning for Business Analytics
28 BMI, CS 776: Advanced BioInformatics
29 CS 766: Computer Vision
30 CS 731: Advanced Artificial Intelligence
31 STAT 771: Statistical Computing
32 STAT 840: Statistical Model Building and Learning
33 CS, ECE 861: Theoretical Foundations of Machine Learning
34 Physics 835: Collider Physics Phenomenology
35 GEN BUS 760: Data Technology for Business Analytics
36 CS 880: Advanced Learning Theory
37 MSE 401/803: Data Science in Materials

The most up-to-date information on each class schedule can be found at: https://enroll.wisc.edu/
Additional information on each course can be found at: https://guide.wisc.edu/courses/