CS 273A: Machine Learning

Winter 2021

Course logistics

  • When: Tuesdays and Thursdays at 2–3:20pm
    • Lectures will be recorded and added to this playlist with access for uci.edu accounts.
  • Where: zoom
  • Announcements and forum: piazza
    • Important course announcements will be made on the forum.
    • Please post on the forum, publicly or privately, all course-related questions (no emails please).
  • Assignments: gradscope
    • Published on this page biweekly.
  • 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 friends.
  • Teaching assistant: Emad Kasaeyan Naein

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: 5%

Schedule

(subject to changes)

(Week) Dates Tuesday Thursday
(1) Jan 5, 7 Introduction Nearest Neighbors
(2) Jan 12, 14 Bayes Classifiers Linear Regression
Assignment 1 due
(3) Jan 19, 21 Linear Regression (cont.) Linear Classifiers
(4) Jan 26, 28 Linear Classifiers (cont.)
Assignment 2 due
VC Dimension
Team roster due
(5) Feb 2, 4 Decision Trees
Neural Networks
Assignment 3 due
(6) Feb 9, 11 Midterm SVMs
(7) Feb 16, 18 Ensemble Methods
Project abstract due
Boosting
Assignment 4 due
(8) Feb 23, 25 Clustering Latent-Space Models
(9) Mar 2, 4 Latent-Space Models (cont.) Reinforcement Learning
Assignment 5 due
(10) Mar 9, 11 Reinforcement Learning (cont.) Final Review
Project report due
(11) Mar 18   Final exam (1:30–3:30pm)

Assigments

  • Assignment 1; due Thursday, January 14 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, copying answers from other students or online resources, allowing other students to copy your answers, communicating exam answers to other students during an exam, or attempting to use 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.