CS 189 Fall 2015: Introduction to Machine Learning

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.


Prof. Alyosha Efros

Prof. Isabelle Guyon

Course Details

Location: 245 Li Ka Shing

Time: Tuesdays & Thursdays, 12:30 pm - 2:00 pm

bCourses (private)

Lecture Notes and Readings (private)

Discussion Section (private)

Assignments (private)

Grades (private)

Course Info & Policies


Course Staff