Machine Learning

CS760, Fall 2021
Department of Computer Sciences
University of Wisconsin–Madison

Logistics

Note: for email, please put [CS760] in the subject title. Thanks!

Course Description

This course is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics, and algorithms of machine learning. Topics covered include supervised learning (neural networks, support vector machines, generative/discriminative learning), unsupervised learning (clustering, GMM, PCA), and reinforcement learning. The course covers theoretical concepts such as inductive bias, geeralization, the PAC learning framework, etc. Assignments include some written exercise and short programming experiments with various learning algorithms.

Prerequisites

Students entering the class are expected to have a background knowledge of probability, linear algebra, and calculus, and have good programming experience. The course will not provide a review on the background knowledge, or tutorials on programming.

These are all optional; accessing one or more of them is a good idea.

Discussion Forum

The instructors and TAs will post announcements, clarifications, hints, etc. on Piazza. You should check the CS760 Piazza page frequently throughout the term. If you have a question, the 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.

The following rules are useful for asking questions on Piazza:

  • Please check if someone has posted the same/similar question before you; it’s much easier if we build on the thread.
  • Use an informative “Summary” line to help others.

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.

Grading

The grading for the course will be be based on (tentative, subject to change):

  • Homework Assignments (8 anticipated): 30%
  • Midterm Exam: 20%
  • Final Exam: 20%
  • Final Project: 30%

General Homework Policies and Academic Misconduct

All homework assignments must be done individually. Cheating and plagiarism will be dealt with in accordance with University procedures (see the Academic Misconduct Guide for Students). For example, code for programming assignments must not be developed in groups, nor should code be shared. You are encouraged to discuss with your peers, the TAs, or the instructor ideas, approaches and techniques broadly, but not at a level of detail where specific implementation issues are described by anyone. If you have any questions on this, please ask the instructor before you act.

Late Homework Policies

Homework assignments will include written problems and sometimes programming. Accounts will be provided on the Computer Sciences Department's instructional Unix workstations located in rooms 1350, 1351, and 1370. Homework is typically due the minute before class starts on the due date. Late submissions will not be accepted. 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. Regrading will be done on the original submitted work, no changes allowed. We will drop the (single) lowest homework score from your final homework average calculation. The drop is meant for emergency situations. We do not provide additional drops, late days, or homework extensions. We encourage you to use a study group for doing your homework. Students are expected to help each other out, and if desired, form ad-hoc homework groups.