This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms. Topics covered include two parts: (1) a gentle introduction of machine learning: generalization and model selection, regression and classification, kernels, neural networks, clustering and dimensionality reduction; (2) a statistical perspective of machine learning, where we will dive into several probabilistic supervised and unsupervised models, including logistic regression, Gaussian mixture models, and generative adversarial networks.
Prerequisites
Appropriate for undergraduate students who have taken CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) or equivalent (e.g. Part 1 covered by Mathematics for Machine Learning).
Instructor: Yuxin Chen <chenyuxin@uchicago.edu>
Format: Pre-recorded video clips + live Zoom discussions during class time and office hours. During lecture time, we will not do the lectures in the usual format, but instead hold zoom meetings, where you can participate in lab sessions, work with classmates on lab assignments in breakout rooms, and ask questions directly to the instructor. Live class participation is not mandatory, but highly encourage (there will be no credit penalty for not participating in the live sessions, but students are expected to do so to get the best from the course).
Teaching staff: Lang Yu <langyu@uchicago.edu> (TA); Yibo Jiang <yiboj@uchicago.edu> (TA); Jiedong Duan <jiedua@uchicago.edu> (Grader)
Lecture hours: Tu/Th, 9:40-11am CT via Zoom (starting 03/30/2021); Please retrieve the Zoom meeting links on Canvas.
Office hours (TA): Monday 9 - 10am, Wednesday 10 - 11am , Friday 10:30am - 12:30pm CT.
Announcements: We use Canvas as a centralized resource management platform. Link: https://canvas.uchicago.edu/courses/35640/
Discussion and Q&A: Via Ed Discussion (link provided on Canvas). The system is highly catered to getting you help quickly and efficiently from classmates, the TAs, and the instructors. Rather than emailing questions to the teaching staff, we encourage you to post your questions on Ed Discussion. For new users, see the following quick start guide: https://edstem.org/quickstart/ed-discussion.pdf
Assignment & Grading: Via Gradescope.
Email policy: We will prioritize answering questions posted to Ed Discussion, not individual emails.
Waitlist: We will not be accepting auditors this quarter due to high demand. Please sign up for the waitlist (https://waitlist.cs.uchicago.edu/) if you are looking for a spot.
Probabilistic Machine Learning: An Introduction; by Kevin Patrick Murphy, MIT Press, 2021. The textbooks will be supplemented with additional notes and readings.
Optional supplementary materials:
Mathematics for Machine Learning; by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Cambridge University Press, 2020.
Pattern Recognition and Machine Learning; by Christopher Bishop, 2006.
The Elements of Statistical Learning (second edition); by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009.