Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.
Prof. Alyosha Efros
Prof. Isabelle Guyon
Location: 245 Li Ka Shing
Time: Tuesdays & Thursdays, 12:30 pm - 2:00 pm
Lecture Notes and Readings (private)
Discussion Section (private)
Course Info & Policies