CS229: Machine Learning


Anand Avati

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 5:30 PM - 6:30 PM (PST) in NVIDIA Auditorium
Quick Links
(You may need to log in with your Stanford email.)
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.

Course Staff

Head Course Assistant
Soyeon Jung
Course Assistants
Arvind Sridhar
Griffin Young
Emmanuel Balogun
Kyu-Young Kim
Siddharth Tanwar