CS229: Machine Learning

Instructors


Tengyu Ma	Chris Ré

Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Course Information

Time and Location

Monday, Wednesday 3:15 PM - 4:45 PM (PST) in NVIDIA Auditorium

Quick Links

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Course Logistics and FAQ
Syllabus and Course Materials
Main Notes (last updated May 17)
Final Project Information
Previous Offerings: Fall 2021, Spring 2021, Fall 2020

Contact and Communication

Due to a large number of inquiries, we encourage you to first read the Course Logistics and FAQ quick link for commonly asked questions, and then create a post on Ed to contact the course staff. Please do NOT reach out to the instructors directly, otherwise your questions may get lost.

This quarter we will be using Ed as the course forum.

All official announcements and communication will happen over Ed.
Any questions regarding course content and course organization should be posted on Ed. You are strongly encouraged to answer other students' questions when you know the answer.
If there are private matters specific to you (e.g special accommodations, requesting alternative arrangements etc.), please create a private post on Ed.
For longer discussions with TAs, please attend office hours.
TA office hours can be found on Canvas. For the course calendar, see also Canvas and the Syllabus and Course Materials quick link.
Before the beginning of the course, please contact the course coordinator for logistical questions (ideally after consulting the FAQ link).