CMSC 25400 / STAT 27725, Winter 2022

Machine Learning

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

Logistics

  • Instructor: Yuxin Chen <chenyuxin@uchicago.edu>

  • Teaching staff: William (Will) Gao <wmg@uchicago.edu>; Xiaoan Ding <xiaoanding@uchicago.edu>

  • Lectures: Tu/Th, 9:30-10:50am CT @ RY 251. Class meeting starting on 01/11/2022 with optional in-person attendance; first two weeks of lectures would be fully virtual -- please retrieve the meeting links on Canvas.

  • Office hours: M: 7-8pm CT (Xiaoan); W: 3-4pm (Will); Th: 8am-9am (Yuxin); F: 1-2pm (Will); Sa: 10-11am (Xiaoan)

  • 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.

Textbooks