Catalog Course 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.

Spring Term

8 hours of Outside Work Hours per week and 3 hours of Instructor presentation of course materials per week and 1 hours of The exchange of opinions or questions on course material per week.

Fall Term

8 hours of Outside Work Hours per week and 3 hours of Instructor presentation of course materials per week and 1 hours of The exchange of opinions or questions on course material per week.