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 in Beckman Institute Auditorium Room 134

• Recitation/OH Schedule

• We will be using Gradescope for managing homeworks and grades. The entry code is on Piazza.

• We will be using Piazza for discussion forums and announcements [link]

• Lectures will be video recorded and posted on Piazza.

• Assignments will be released on GitHub [link] 

• 6 Homeworks (worth approximately 60% of final grade)

• 3 Miniprojects (worth approximately 30% of final grade)

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

Instructors

Please direct administrative/general questions to Piazza and the TAs first.

Lecturers

• Dr. Umaa Rebbapragada [JPL] [GScholar] 

• Jake Lee [JPL] [Personal] [GScholar] 

• Dr. Lukas Mandrake [JPL] [GScholar] 

Teaching Assistants

• Emile Anand [Head TA]

• Sreemanti Dey

• Shivansh Gupta

• Agnim Agarwal

• Julio Arroyo Ibarra

• Cloris Cheng

• Prashanth Mohan

• Rishi Gundakaram

• Derek Ing

• Helen Shen

• Clara Wang

• Shenyi Li

• Rajeev Datta

• Max Bricken

• Saraswati Soedarmadji

Advisor

• Dr. Yisong Yue (On Sabbatical) [Caltech] [Personal]