Machine Learning & Data Mining

Course Description

This course will cover popular methods in machine learning and data mining, with an emphasis on developing a working understanding of how to apply these methods in practice. This course will also cover core foundational concepts underpinning and motivating modern machine learning and data mining approaches. This course will also cover some recent research developments.

Recommended prerequisites: algorithms, linear algebra, calculus, probability, and statistics (CS/CNS/EE/NB 154 or CS/CNS/EE 156a or instructor’s permission)

Logistics

• Lectures on Tu/Th at 2:30pm-4pm over Zoom [link posted on Piazza]. Lectures will be recorded. [Lectures/OH schedule]

• Recitations over Zoom. [Lectures/OH schedule]

• We will use Piazza for discussions and announcements. [link]

• We will use Gradescope for managing homeworks and grades. [link]

• 6 Homeworks (worth approximately 60% of final grade) [Assignments page]

• 3 Mini-projects (worth approximately 30% of final grade) [Assignments page]

• Final Exam (worth approximately 10% of final grade)

Teaching Team

Instructors

We are very fortunate to have talented researchers from the Amazon Pasadena office as lecturers this term:

• Alessandro Achille (https://alexachi.github.io/)

• Ankan Bansal (https://ankanbansal.com/)

• Giovanni Paolini (https://www.giovannipaolini.org/)

• Vijay Mahadevan (https://www.amazon.science/author/vijay-mahadevan)

• Aruni RoyChowdhury (https://arunirc.github.io/)

Teaching Assistants

• Charles Guan. Head TA, administrative/general questions. cguan@

• Ayooluwa Odemuyiwa

• Charlotte Park

• Chase Blagden

• Daniel Israel

• Hannah Chen

• Haoxuan Chen

• Meena Hari

• David Jin

• Megan Tjandrasuwita

• Pantelis Vafeidis

• Sarah Zou

• Shenyi Li

Official Liaison

• Yisong Yue (on sabbatical)