Course description: Machine learning is a multi-faceted area of research, with many subfields that have been explored over the past 50 years. This class will provide an overview of the field, with an emphasis on core statistical foundations. Machine learning is the formalized study of the intuitive notion of "learning" (which itself can be interpreted in a myriad of ways, from "self-improvement" to "pattern discovery"). Some popular models of learning include classification and supervised learning, developmental and evolutionary learning, semi-supervised learning, reinforcement learning, and unsupervised learning.

The course will stress foundations that underly these seemingly disparate forms of learning. Most computational models of learning reduce to extraction of patterns or regularities by combining background knowledge, observed data, and stored experience. Formally, this process can be modeled as projecting or embedding the data onto some measurable hypothesis space (e.g, a parametric probability distribution, a vector space with an inner product defined on it, or a manifold or graph). In this course, we will cover three approaches in detail: parametric approaches based on Bayesian inference and graphical models; nonparametric approaches based on kernel methods; and finally, spectral techniques based on matrix theory.

Lectures: Monday & Wednesday 2:05-3:20, Room 140, CS Building

Prerequisites: Good undergraduate level exposure to basic concepts in artificial intelligence; linear algebra, probability theory and statistics, algorithmic analysis; knowledge of MATLAB and/or R (helpful, not required), and high-level (C, Java etc.) computer programming. Please talk with the instructor if you want to take the course but have doubts about your qualifications. This is a core class for computer science graduate students.

Credit: 3 units

Instructor: Professor Sridhar Mahadevan (mahadeva AT cs DOT umass DOT edu)

• Office hours: Monday/Wednesday 10:00-11:00 a.m. (or by appointment), Room 204, CS Building

Teaching assistant: Peter Krafft (pkrafft AT cs DOT Umass DOT edu)

• Office hours: Tue 10-11 a.m, Thursday 2:30-3:30 p.m, LGRT 220