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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