CS760 Machine Learning Fall 2019

This course provides an introduction to the theory and practical methods for machine learning, and is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics and algorithms of machine learning. Topics covered include nearest neighbor method, decision tree learning, Support Vector Machines, Bayesian networks, neural networks and deep learning, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, mistake bounds, etc. Lectures TuTh 11:00AM - 12:15PM in EDUCATION L196, see calendar below Professor: Jerry Zhu, jerryzhu@cs.wisc.edu, Office hour Fridays 2:30-3:30pm, CS 6391 Teaching Assistants: Swati Anand, sanand24@wisc.edu Office hour Thursdays 4pm, CS 4205 Ashwin Tayade, tayade@wisc.edu Office hour Tuesdays at 4pm, CS 4296 Graders: Arpit Jain, ajain74@wisc.edu Sushma Kudlur Nirvanappa, kudlurnirvan@wisc.eduPrerequisitesStudents entering the class are expected to have a background knowledge of probability, linear algebra, and calculus, and have good programming experience.Textbooks (optional)Pattern Recognition and Machine Learning, Chris Bishop. Machine Learning, Tom Mitchell. Understanding Machine Learning: From Theory to Algorithms, Shalev-Shwartz, Ben-David.Grading:Homeworks (60%), exams (40%)Discussion forum:The instructors and TAs will post announcements, clarifications, hints, etc. on Piazza. Hence you must check the CS760 Piazza page frequently throughout the term. If you have a question, your best option is to post a message on Piazza. The staff (instructors and TAs) will check the forum regularly, and if you use the forum, other students will be able to help you too. When using the forum, please do not post answers to homework questions before the homework is due. If your question is personal or not of interest to other students, you may mark your question as private on Piazza, so only the instructors will see it. If you wish to talk with one of us individually, you are welcome to come to our office hours. Please reserve email for the questions you can't get answered in office hours or through the forum. CS760 PiazzaHomeworksPlease submit hw pdf via UW-Madison's Canvas system.

- Homework 1 pdf, latex template. Due 9/10.
- Homework 2 pdf, latex template, D1.txt, D2.txt, D3leaves.txt, Dbig.txt, Druns.txt. Due 9/24.
- Homework 3 assigned in Canvas. Due 10/3.
- Homework 4 assigned in Canvas. Due 10/10.
- Homework 5 assigned in Canvas. Due 10/17.

- Exam 1: Wed Oct. 23 5:30-7pm, Noland 132.
- Exam 2: Mon Dec. 16, 2019 7:25-9:25pm, PSYCHOLOGY 105