Department: 
Course Number: 
189
CCN: 
26684
Instructor: 
Malik
Units: 
4
Description: 
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.
Semester: 
Spring 2013
Term (SP for Spring; FL for Fall): 
SP