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 14:30-15:55 at  134 (Auditorium) BCK. Lectures will be recorded. [Lectures]

• Check Calendar for Office Hours

• Recitations are scheduled according to [Schedule]. The time and location will also be announced on Piazza.  

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

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

Instructor

• Yisong Yue 

Teaching Assistants

• Natalie Simon-Bernat. Head TA, administrative/general questions. nbernat@caltech.edu

• Anwesha Das

• Anna Szczuka

• Christina Liu

• Daniel Khalil

• Dominic Phung

• Ishita Mathur

• Madeline Egan

• Raaghav Malik

• Ryan Lin

• Stephen Ebaseh-Onofa

• Sidd Ojha

• Sanvi Pal

• Shrujana Kunnam

• Yingying Gong