CS760 Machine Learning Spring 2026
This course provides an introduction to the mathematical and algorithmic methods for machine learning, and is designed to give a graduate-level student a good grounding in how machine learning works. Lectures: Tu,Th 9:30-10:45am in Morgridge Hall 1524 Canvas, Piazza Professor: Jerry Zhu, jerryzhu@cs.wisc.edu, office hour Thursdays 4-5pm, Morgridge Hall 5520 Teaching Assistants: Avi Trost, atrost@cs.wisc.edu, office hour Mondays 5:30-6:30, Morgridge Hall 5581 Michael Yu, mcyu@wisc.edu, Tuesdays 11am-12pm, Morgridge Hall 5581 Schedule 01/20 course overview (reading: Jordan & Mitchell) 01/22 machine learning overview 01/27 k-nearest-neighbor, decision tree 01/29 evaluation 02/03 parametric models: logistic regression, naive Bayes 02/05 linear regression 02/10 optimization in machine learning 02/12 clustering 02/17 dimensionality reduction 02/19 neural network 02/24 backpropagation 02/26 other training considerations 03/03 convolutional neural network 03/05 recurrent neural network 03/10 midterm review 03/12 transformer 03/17 diffusion models 03/19 bias vs. variance 03/24 Probably Approximately Correct learning 03/26 Vapnik-Chervonenkis dimension 04/07 support vector machines 04/09 learning theory on deep learning 04/14 reinforcement learning 04/16 Q-learning 04/21 policy gradient 04/23 semi-supervised learning, active learning, self-supervised learning 04/28 transfer learning 04/30 learning in game theory Prerequisites Students entering the class are expected to have a background knowledge of probability and statistics, linear algebra, calculus, and have good programming experience. Textbooks (optional) Machine Learning. Tom Mitchell. Pattern Recognition and Machine Learning. Chris Bishop. Understanding Machine Learning: From Theory to Algorithms. Shalev-Shwartz, Ben-David. Deep Learning. by Goodfellow, Bengio, Courville. Reinforcement Learning: An Introduction. Sutton, Barto. Grading: Class participation (10%), homework (20%), midterm exam (30%), final exam (40%) Homework: Assignment grading questions must be raised with the TAs within 72 hours after it is returned. Regrading request for a part of a homework question may trigger the grader to regrade the entire homework and could potentially take points off. We will drop one lowest homework score from your final homework average calculation. The drop is meant for emergency. It is unlikely that we provide homework extensions. Exams: Topics: everything up to the time of the exam, including slides, notes, selected readings. Plan for exams at these times and let us know about any exam conflicts during the first two weeks of the semester. If an emergency arises that conflicts with the exam times, email us as soon as possible. Emergency exam conflicts will be handled on a case-by-case basis. Exam grading questions must be raised with the instructor within 72 hours after it is returned. Midterm: TBA Final: 5/5/2026, Tuesday 12:25PM - 2:25PM. Room TBA All exams are closed book. Absolutely no phone/AI use. Bring a calculator and copious amount of blank scratch paper. For midterm, one sheet of printer paper with notes on both sides allowed (handwritten or typed). For final exam, two such sheets are allow (e.g. you can re-use the midterm sheet). Lectures and readings on the syllabus page are required, with a few exceptions (to be posted before the exam). You are responsible for topics covered in lecture even if there are no lecture notes on the topic. Academic Integrity: By virtue of enrollment, each student agrees to uphold the high academic standards of the University of Wisconsin–Madison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary sanctions include, but are not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension or expulsion. UW-Madison's Academic Misconduct Process. We encourage you to use AI tools for self-learning, but homework and exams should be done individually without using AI. Student Health, Well-being, and Basic Needs Students often experience stressors outside the classroom that can impact their academic experience. These may include mental and physical health concerns; difficulty securing food, housing, and other basic needs; misuse of alcohol or other drugs; sexual or relationship violence; family challenges; and campus climate, among others. If you’re experiencing one or more of these challenges, you’re not alone and help is available. To learn more, visit Get Help.