cs229.stanford.eduCS229: Machine Learning

CS229 Instructors Emily Fox Sanmi Koyejo 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 Lectures: Mon, Wed 1:30 PM - 2:50 PM (PT) at NVIDIA Auditorium Quick Links (You may need to log in with your Stanford email.)Course Logistics and FAQ Syllabus and Course Materials Final Project Information Previous Offerings: Fall 2023 , Summer 2023 , Spring 2023 , Fall 2022 , Summer 2022 , Spring 2022 , Fall 2021 , Spring 2021 , Fall 2020 Contact and Communication Ed is the primary method of communication for this class. Please do NOT reach out to the instructors (or course staff) directly, otherwise your questions may get lost. Due to a large number of inquiries, we encourage you to first read the "Course Logistics and FAQ" quick link above for commonly asked questions, and then create a post on Ed to contact the course staff. 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 above. Before the beginning of the course, please contact the course coordinator for logistical questions (ideally after consulting the FAQ link). Course Staff To help with project advice, each member of course staff’s ML expertise is also listed below. Course Manager John Cho Head Course Assistant Zhoujie Ding NLP, LLMs Course Assistants Rishi Agarwal NLP, RL Samir Agarwala CV, Scene Understanding, Graphics Winnie Chow LLMs Sonia Chu CV, NLP Rishi Desai CV, Ensemble Models, LLMs Kefan Dong RL Theory, ML Theory, LLMs Jacob Frausto CV, Robotics, RL Hong Jun Jeon Info Theory, ML Theory, RL Priya Khandelwal Multimodal Learning, LLMs, MLSys Hermann Kumbong MLSys, LLMs Rylan Schaeffer LLMs John So Robotics, RL, CV Alex Wang Time Series, AI+Healthcare Zedian Xiao LLMs, RL Shijia Yang 3D Vision, Multimodal Learning Eric Zhang CV, NLP,...