CS 7641 & 4641
College of Computing Building
Charles Isbell, firstname.lastname@example.org
224, Technology Square Research Building, 385-6491
Office Hours: TBA, and by appointment
Christopher Simpkins, simpkins@cc
CCB 2nd Floor open meeting area
Office Hours: TTh 12:30-1:00, and by appointment
Ravi Sastry Ganti Mahapatruni, gmravi2003@gatech
Office Hours: F 1:30-3:00, and by appointment
Machine Learning is a three-credit course on, well, Machine Learning. Machine Learning is that area of Artificial Intelligence that is concerned with computer programs that modify and improve their performance through experience. The area is concerned with issues both theoretical and practical. This particular class is a part of a series of classes in the Intelligence thread and Intelligent Systems area, and as such takes care to present algorithms and approaches in such a way that grounds them in larger systems. We will cover a variety of topics, including: statistical supervised and unsupervised learning methods, randomized search algorithms, Bayesian learning methods, and reinforcement learning. The course also covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a semester-long research project.
There are four primary objectives for the course:
To provide a broad survey of approaches and techniques in ML
To develop a deeper understanding of several major topics in ML
To develop the design and programming skills that will help you to build intelligent, adaptive artifacts
To develop the basic skills necessary to pursue research in ML
The official prerequisite for this course is an introductory course in artificial intelligence. In particular, those of you with experience in a general representational issues in AI, some AI programming, and at least some background (or barring that, willingness to pick up some background) in statistics and information theory should be fine. Any student who did well in an AI course like this one should be fine. You will note that the syllabus for that particular course suggests at least some tentative background in some machine learning techniques as well. Having said all that, the most important prerequisite for enjoying and doing well in this class is your interest in the material. I say that every semester and I know it sounds trite, but it's true. In the end it will be your own motivation to understand the material that gets you through it more than anything else. If you are not sure whether this class is for you, please talk to me.
I reserve the right to modify any of these plans as need be during the course of the class; however, I won't do anything capriciously, anything I do change won't be too drastic, and you'll be informed as far in advance as possible.