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.

* Enrollment will not expand until after TA hiring has been completed - not sure when that will be, likely during phase 2.

* Time conflicts are allowed but NO alternate final exam!

* Lecture will be recorded for playback later.

Requisites

• Undergraduate Students: College of Engineering declared majors or L&S Computer Science or Data Science BA

Credit Restrictions

Students will receive no credit for Comp Sci 189 after taking Comp Sci 289A.

Repeat Rules

Course is not repeatable for credit.