CS229: Machine Learning

Fall 2020


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
Mon, Wed 10:00 AM – 11:20 AM on zoom.
Contact and Communication
Due to a large number of inquiries, we encourage you to read the logistic section below and the FAQ page for commonly asked questions first, before reaching out to the course staff.
Piazza is the forum for the class.
  • All official announcements and communication will happen over Piazza.
  • Any questions regarding course content and course organization should be posted on Piazza. 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 Piazza.
  • For longer discussions with TAs, please attend office hours.
  • TA office hours and the course calendar can be found here.
  • Before the beginning of the course, please contact the course coordinator Amelie Byun for logistical questions (ideally after consulting the FAQ page)


    The logistics information can be found here. The zoom link to lectures can be found in the "syllabus" section on canvas.

Teaching Assistants