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.
Appropriate for undergraduate students who have taken
CMSC 25300 & Statistics 27700 (Mathematical Foundations of Machine Learning) (for CS major)
OR STAT 24300 or STAT 24500 (for statistics major)
OR equivalent (e.g. Part 1 covered by Mathematics for Machine Learning).
Instructors and teaching assistants:
Yuxin Chen (Instructor) <chenyuxin@uchicago.edu>
Zixin Ding (TA) <zixin@uchicago.edu>
Mourad Heddaya (TA) <mourad@uchicago.edu>
Chaoqi Wang (TA) <chaoqi@uchicago.edu>
Rajat Gupta (Grader) <rajatgupta@uchicago.edu>
Lectures: Tu/Th, 9:30-10:50am CT @ Eckhardt 202. First session starting on 01/3/2023.
Office hours:
Th: 11am-noon (Yuxin)
M/Tu/W/F/Sa (TA OH's to be updated)
Announcements: We use Canvas as a centralized resource management platform.
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.
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.