(Taken from the UC Berkeley Course Guide)
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.
MATH 53, MATH 54, CS 70 or consent of instructor
3 hours of lecture and 1 hour of discussion per week.