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.

* Lecture will be recorded for playback later but time conflicts are still NOT allowed.

* NO alternate final exam will be offered.

* Interested DS majors should expect communication directly from the Data Science Advisors about enrolling in CS 189.

Requisites

• Undergraduate Students: College of Engineering declared majors and L&S Computer Science

Credit Restrictions

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

Repeat Rules

Course is not repeatable for credit.