This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are:
Location and Time
Warren Weaver Hall Room 102,
251 Mercer Street.
Tuesdays 5:10 PM - 7:00 PM.
Familiarity with basics in linear algebra, probability, and analysis of algorithms.
Projects and Assignments
There will be 3 to 4 assignments and a project. The final grade is essentially the average of the assignment and project grades. The standard high level of integrity is expected from all students, as with all math and computer science courses.
The following is the required textbook for the class. It covers all the material presented (and a lot more):
An extensive list of recommended papers for further reading is provided in the lecture slides.