
Course description: As the world increasingly relies on massive digital datasets for scientific exploration, commerce, social interaction, entertainment, and surveillance, the analysis of such data has become a problem of deep scientific and technological interest. Machine learning is an exciting interdisciplinary field that is central to the analysis of massive datasets. It is a multifaceted 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 realworld applications. Machine learning is the formalized study of the intuitive notion of "learning" (which itself can be interpreted in a myriad of ways, from "selfimprovement" to "pattern discovery"). The course will cover three major models of learning  unsupervised learning, supervised learning, and reinforcement learning  as well as some minor variants, such as semisupervised 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 linear algebra and matrices.
Lectures: Monday & Wednesday 2:303:45, Room 142, CS Building
Course Schedule, Projects, Reading etc.Prerequisites: Good undergraduate level exposure to basic concepts in artificial intelligence; linear algebra, probability theory and statistics, algorithmic analysis; familiarity with highlevel programming languages, such as Python, C++, Java etc.; knowledge of MATLAB and/or R (helpful, not required). 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.
Other Books and Reference Material
Credit: 3 units
Instructor: Professor Sridhar Mahadevan (mahadeva AT cs DOT umass DOT edu)
Teaching assistant:
Any plagiarism reported by the TAs will be dealt with according to the official policy of the College of Information and Computer Sciences, and will likely result in an F for the course. The final group project will be undertaken by a small group (23) of students working together.
Grading: