This course provides a systematic introduction to machine learning, covering theoretical as well as practical aspects of the use of statistical methods in artificial intelligence. Topics include linear models, support vector machines, regularization theory, and graphical models. Application examples are taken from areas like information retrieval, natural language processing, computer vision and others.For a more detailed description of the course see the Syllabus page. An up-to-date repository of course material, including lecture slides, is kept on the Calendar page.
Greg Shakhnarovich (gregory @ cs)
Dan Grollman (dang @ cs)
Payman Yadollahpour (pyadolla @ cs)
Emailing TAs: (cs195-5tas @ cs)
When? (and where?)Lectures: Monday, Wednesday, Friday 11:00 - 11:50 AM (D Hour)
Location: Lubrano (CIT 4th floor)
Office hours Greg: by e-mail appointment
Dan: CIT 315, Friday 1pm-3pm
Payman: CIT 303, Monday 1pm-3pm
Grading: There will be six homework assignments, each worth 10% of the final grade. In addition, there will be two in-class exams: a midterm (15% of the grade) and a final (20%). The remaining 5% will be determined by the teaching staff based on class participation.
Some discussion among students regarding homework is encouraged. Appropriate discussion focuses on understanding the relevant material, and exchanging ideas on how to approach a problem -- not direct cooperation on solving the homework. In the end each student should do all the homework problems and submit her/his own solution.
200-level credit will be given for an individual final project. Projects will consist of either design and implementation of a machine learning solution for a practical application, or an in-depth investigation of a machine learning topic. We will suggest some problems, but students will also be encouraged to suggest other problems that interest them. The main outcome will be a write-up in the style of a conference paper, with emphasis on motivation and discussion.