CS M146 (Winter 2021) Discussion Materials

Note: This course is no longer updated since March 19, 2021. Slides for my discussion sessions are still publicly available in this webpage.

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).

• 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] 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] Problem set 1 has been released on CCLE on Jan. 15 and due on 11:59 PM PST, Jan. 29.

• [Jan. 22] 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

• [Jan. 29] Week 4’s quiz due/close date and time: Jan 31, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Jan. 31 and complete on GradeScope in time.

• [Jan. 30] Problem set 2 has been released on CCLE on Jan. 30 and due on 11:59 PM PST, Feb 12.

• [Feb. 5] Week 5’s quiz due/close date and time: Feb 7, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Feb. 7 and complete on GradeScope in time.

• [Feb. 12] Problem set 3 has been released on CCLE on Feb. 12 and due on 11:59 PM PST, Feb 26.

• [Feb. 12] Week 6’s quiz due/close date and time: Feb 14, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Feb. 14 and complete on GradeScope in time.

• [Feb. 18] Week 7’s quiz due/close date and time: Feb 21, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Feb. 21 and complete on GradeScope in time.

• [Feb. 26] Week 8’s quiz due/close date and time: Feb 28, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Feb. 28 and complete on GradeScope in time.

• [Mar. 4] Problem set 4 has been released on CCLE on Feb 26 and due on 11:59 PM PST, Mar 12.

• [Mar. 4] Week 9’s quiz due/close date and time: Mar 7, 2021 (Sunday) 11:59 PM PST. Please start the quiz before 11:00 PM PST, Mar 7 and complete on GradeScope in time.

• [Mar. 13] Practice questions for final together with the solutions are available on CCLE, under the tab of “Week 10” section.

• [Mar. 13] Final exam will be released on GradeScope, starting at 8:00 am PST, March 15 and closed at 8:00 am PST, March 16. Important updates on the final exam has been posted on Campuswire. Pleae check Post #437 and Post #525!

• [Mar. 13] [Course closure] The course will be closed on Friday, March 19. Thanks for everyone’s participation. Have a safe and enjoyable spring break!

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	Logistic Regression, Linear Regression	Week 4
Feb. 5	Overfitting and regularization, Neural Nets (Part I)	Week 5
Feb. 12	Neural Nets (Part II), Learning theory, Kernels, PyTorch	Week 6, Week 6 PyTorch
Feb. 19	SVM [SVM Notes from Stanford], [SVM Slides from NYU]	Week 7
Feb. 26	Ensemble Method, Multi-Class Classification, ML Evaluation	Week 8
Mar. 5	Naive bayes, Clustering (K-means, GMM)	Week 9
Mar. 12	PCA, HMM, Final Q&A	Week 10
Mar. 14	Final exam preparation and practice (Practice exam and solution available on CCLE)	Discussion slides collection