CS 273A: Machine Learning
Fall 2021
Course logistics
- When: Tuesdays and Thursdays at 11am–12:20
- Where:
- About half the lectures will be in-person and half virtual; please see the schedule below for the planned (subject to change) location of each lecture.
- In-person: SH 128. Links to last year’s recordings of the same syllabus will be uploaded to this page (with access for uci.edu accounts).
- Virtual: zoom. These lectures will be recorded and uploaded to this page (with access for uci.edu accounts).
- Announcements and forum: ed discussion
- Important course announcements will be made on the forum.
- Please post on the forum, publicly or privately, all course-related questions.
- Please do not email course staff, except for personal matters unrelated to the course.
- Assignments: gradescope
- Assignments will be uploaded to this page.
- Teams, reports, and grades: canvas
- Instructor: Prof. Roy Fox
- Office hours: calendly
- Enrolled students are welcome to:
- Schedule 15-minute slots (more than once if needed);
- Give at least 4-hour notice;
- Attend individually or with classmates.
- Teaching assistant: Xiangyi Yan
- Office hours: calendly
Grading policy
- Assignments: 40%
- 4 best of 5 assignments count for 10% each.
- No late submission.
- Exams: 40%
- Midterm: 18%
- Final: 22%
- Project: 15%
- Team roster: 1%
- Abstract: 2%
- Report: 12%
- Participation (in-class, on-forum, evaluations): 5%
Schedule
(p.) = in-person; (v.) = virtual
Note: the planned schedule is subject to change.
Assignments
- Assignment 1; due Thursday, October 7, 2021 (Pacific Time).
- Assignment 2; due Tuesday, October 19, 2021 (Pacific Time).
- Assignment 3; due Tuesday, November 2, 2021 (Pacific Time).
- Assignment 4; due Friday, November 12, 2021 (Pacific Time).
- Assignment 5; due Tuesday, November 30, 2021 (Pacific Time).
Resources
Books
- Hal Daumé III, A Course in Machine Learning
- Kevin Murphy, Probabilistic Machine Learning
- Richard Duda et al., Pattern Classification
- Christopher Bishop, Pattern Recognition and Machine Learning
- Trevor Hastie et al., The Elements of Statistical Learning