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: linear algebra, calculus, probability, statistics, algorithms. (in order of importance)

Problem Set 1: https://drive.google.com/file/d/1Gz7Od4Ht2MvZAj_25iTzgpt6MnTFGXn8/view?usp=sharing

All future sets and communication will happen on Piazza, so please make sure you enroll with Regis.

Logistics

• Lectures on Tu/Th 13:00-14:25 at  105 Annenberg. 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] (Also via Canvas)

• We will use Gradescope for managing homeworks and grades. [Gradescope link] (Also via Canvas)

• 6 Homeworks (worth 30% of final grade, graded only for completeness not correctness) [Assignments page]

• 6 In-class Quizzes (worth 30% of final grade, following each homework)

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

• In-class Final Exam (worth 10% of final grade)

Teaching Team

Instructor

• Yisong Yue, Lead Instructor

Teaching Assistants

• Hongqiao (Harry) Chen, Lead TA

• Levi Alderete

• Anahita Eshghetorki

• Arjun Sharma

• Idil Turasi

• Allison Xin

• Dhruv Verma

• Amanda Wang

• Ellie Wang

• Olivia Wang

• Natalie Zhou