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: gradescope
- 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
- 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: 5%

Schedule

(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.)	Regularization
(4) Jan 26, 28	Linear Classifiers Assignment 2 due	VC Dimension
(5) Feb 2, 4	Decision Trees Team roster due	Mid-Term Review Assignment 3 due
(6) Feb 9, 11	Midterm	Neural Networks
(7) Feb 16, 18	SVMs (Feb 18) Project abstract due	Ensemble Methods (recorded)
(8) Feb 23, 25	Clustering Assignment 4 due	Latent-Space Models
(9) Mar 2, 4	Latent-Space Models (cont.)	Active and Online Learning Assignment 5 due
(10) Mar 9, 11	Reinforcement Learning	Final Review Project report due
(11) Mar 18		Final exam (1:30–3:30pm)

Assignments

Assignment 1; due Thursday, January 14, 2021 (Pacific Time).
Assignment 2; due Tuesday, January 26, 2021 (Pacific Time).
Assignment 3; due Thursday, February 4, 2021 (Pacific Time).
Assignment 4; due Tuesday, February 23, 2021 (Pacific Time).
Assignment 5; due Thursday, March 4, 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

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.