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
http://www.eecs.berkeley.edu/~efros
Prof. Peter Bartlett
http://www.eecs.berkeley.edu/~bartlett
bCourses (private)
Lecture Notes and Readings (private)
Discussion Section (private)
Assignments (private)
Grades (private)