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

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

(Week) Dates Tuesday Thursday
(0) Sep 23 Introduction (p.)
Winter'21 video:
(1) Sep 28, 30 Nearest Neighbors (p.)
Winter'21 video: 		Bayes Classifiers (v.)
Winter'21 video:
(2) Oct 5, 7 Linear Regression (v.)
Stochastic Gradient Descent:
Winter'21 video: 		Linear Regression (cont.) (p.)
Winter'21 video:
Assignment 1 due
(3) Oct 12, 14 Regularization (p.)
Winter'21 video: 		Linear Classifiers (p.)
Winter'21 video:
(4) Oct 19, 21 Decision Trees (video)
Winter'21 video:
Team roster due
Assignment 2 due 		Logistic Regression (v.)
Winter'21 video:
(5) Oct 26, 28 VC Dimension (v.) Midterm Review (p.)
(6) Nov 2, 4 Support Vector Machines (p.)
Winter'21 video:
Assignment 3 due 		Midterm exam (p.)
(7) Nov 9, 11 Ensemble Methods (v.)
Winter'21 video: 		-- Veterans Day --
Assignment 4 due Friday
(8) Nov 16, 18 Clustering (p.)
Winter'21 video:
Project abstract due		 Latent-Space Models (p.)
Winter'21 videos:
(9) Nov 23, 25 Active and Online Learning (p.)
Winter'21 video: 		-- Thanksgiving --
(10) Nov 30, Dec 2 Reinforcement Learning (p.)
Winter'21 video:
Assignment 5 due 		Final Review (p.)
(11) Dec 7, 9 Final exam (10:30am–12:30) Project report due

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

Academic honesty

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.