CS 273A: Machine Learning
- When: Tuesdays and Thursdays at 11am–12:20
- 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
- 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%
(p.) = in-person; (v.) = virtual
Note: the planned schedule is subject to change.
- 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).
- 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
Don’t cheat. Academic honesty is a requirement for passing this class. Compromising the academic integrity of this course is subject to a failing grade. The work you submit must be your own. Academic dishonesty includes, among other things, partially copying answers from other students or online resources, allowing other students to partially copy your answers, communicating information about exam answers to other students during an exam, or attempting to use disallowed notes or other aids during an exam. If you do so, you will be in violation of the UCI Policy on Academic Honesty and the ICS Policy on Academic Honesty. It is your responsibility to read and understand these policies, in light of UCI’s definitions and examples of academic misconduct. Note that any instance of academic dishonesty will be reported to the Academic Integrity Administrative Office for disciplinary action, and may fail the course.