CS229: Machine Learning
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, etc); unsupervised learning (clustering, dimensionality reduction, etc); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning. Where appropriate, 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.
