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/