Note: This course is currently active and updated.
CS M146 Final exam schedule: March 15, 2021. Note that daylight saving time 2021 in California will begin at 2:00 AM on Sunday, March 14!
Course Info
Course name: Introduction to Machine Learning
Instructor: Sriram Sankararaman
Lecture time & location: Lec 1, Monday/Wednesday 12-1:50PM (Los Angeles), Online (Recorded, Zoom links provided for enrolled students).
Course website: TBA
Zoom Link: Please find on CCLE for enrolled students.
Course Forum: Campuswire (Invitation sent to enrolled students).
TA Info
TA: Junheng Hao
TA office hours: Monday 3-5PM (US Pacific/Los Angeles)
Contact: haojh [DOT] ucla [AT] gmail [DOT] com (for CS146 only, and please add “CS146” in the subject of the email). You can also DM me through Campuswire. Note: Please do NOT send to other emails.
Discussion Info (Dis 1C, instructed by Junheng)
Time: Fridays 12-1:50PM
Location: Online (Recorded, Zoom links provided on CCLE/Compuswire for enrolled students)
Recording: Available on CCLE under the section of each week, named “WeekX_Dis1C_Junheng”
Announcements
- [Jan. 1] Welcome to CS M146: Introduction to Machine Learning. Greetings from Week 0!
- [Jan. 4] For all enrolled students: Please register on Campuswire (as course forum) and Gradescope (for problem sets, quizzes and exams). Some private course materials (such as recordings) are on CCLE.
[Jan. 8] Week 1’s math quiz due/close date and time: Jan 10, 2021 (Sunday) 11:59 PM PST. Please complete on GradeScope in time.
[Jan. 15] [New] Week 2’s quiz due/close date and time: Jan 17, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Jan. 17 and complete on GradeScope in time. Campuswire Post
[Jan. 15] [New] Problem set 1 will be released on CCLE on Jan. 15 and due on 11:59 PM PST, Jan. 29.
[Jan. 22] [New] Week 3’s quiz due/close date and time: Jan 24, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Jan. 24 and complete on GradeScope in time. Campuswire Post
Discussion Materials
| Date | Content | Slides & Links |
|---|---|---|
| Jan. 8 | Course logistics and overview. Math review: [Probability], [Linear Algebra], [Optimization 1], [Optimization 2], [Math essentials from UW] | Week 1, Week 1 (Math) |
| Jan. 15 | Decision trees, nearest neighbors and linear classification. Programning Prep. | Week 2, Colab Demo |
| Jan. 22 | Perceptron, Logistic Regression, Linear Models, Optimization | Week 3 |
| Jan. 29 | TBA | Week 4 |