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 traineeships. Students are encouraged to discuss these opportunities with their potential graduate advisors during the application process.



BMI/CS 576: Intro Bioinformatics BMI/CS 776: Advanced BioInformatics
CS/ECE/ISYE 524 Introduction to Optimization CS/ECE/ME 532: Theory and Applications of Pattern Recognition
CS/ECE 533: Image Processing CS 540: Intro to Artificial Intelligence
CS 545: Natural Language and the Computer CS 731: Advanced Artificial Intelligence
ECE 729: Information Theory CS 760: Machine Learning
CS/ECE 761: Mathematical Foundations of Machine Learning CS 766: Computer Vision
CS 769: Advanced Natural Language Processing STAT 771: Statistical Computing
STAT 451: Introduction to Machine Learning and Statistical Pattern Classification STAT 453: Introduction to Deep Learning and Generative Models
ECE/CS/ME 539: Introduction to Artificial Neural Network and Fuzzy Systems STAT 840: Statistical Model Building and Learning
STAT 860: Estimation of Functions from Data CS/ECE 861: Theoretical Foundations of Machine Learning 
STAT 679: Computing Tools for Data Analytics PHYSICS 835: Collider Physics Phenomenology
CS 639: Introduction to Computational Learning Theory CS 880: Advanced Learning Theory
GEN BUS 656: Machine Learning for Business Analytics GEN Bus 760: Data Technology for Business Analytics