This course introduces several fundamental concepts and methods for machine learning. The objective is to familiarize the audience with some basic learning algorithms and techniques and their applications, as well as general questions related to analyzing and handling large data sets. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus on machine learning algorithms and applications, with some broad explanation of the underlying principles. The main topics covered are:
Location and Time
Room 109 Warren Weaver Hall,
251 Mercer Street.
Mondays and Wednesdays 3:30PM - 4:45PM.
The course will introduce all basic concepts needed in probability and statistics.
Projects and Assignments
There will be a mid-term exam, about 5 assignments, and a project. The final grade is essentially the average of the exam, assignments, and project grades. The standard high level of integrity is expected from all students, as with all CS courses.
There is no single textbook covering the material presented in this course. Here is a list of books recommended for further reading in connection with the material presented:
Here is list of some of the software tools used to illustrate the applications of the algorithms discussed:
An extensive list of recommended papers for further reading will be provided in the lecture slides.