Machine Learning In Practice

Instructor: Carolyn Penstein Rose

Language Technologies Institute/Human-Computer Interaction Institute
Newell Simon Hall 4531
Carnegie Mellon University
Pittsburgh, PA 15213

Phone: (412) 268-7130
Fax: (412) 268-6298
Other Courses:

Last Updated: November 5, 2007

You can download the full syllabus from here.

In Person Version Vs. Self-Paced Distance Version

Machine Learning in Practice is offered each Fall at Carnegie Mellon University as an in person course, with lectures that meet on campus twice a week, TA sessions, and in person meetings with the TA or instructor by appointment.

Spring and and Spring+Summer each year this course is offered as a self-paced distance course, which is available both to Carnegie Mellon Students as well as students from other universities. On-line video lectures are made available to students who are registered for the course. Students can work through the material at their own pace. On line mentoring sessions are held each week. Students who are in the Pittsburgh area can also make appointments to meet one-on-one with the instructor.

Course Decription

Machine Learning is concerned with computer programs that enable the behavior of a computer to be learned from examples or experience rather than dictated through rules written by hand. It has practical value in many application areas of computer science such as on-line communities and digital libraries. This class is meant to teach the practical side of machine learning for applications, such as mining newsgroup data or building adaptive user interfaces. The emphasis will be on learning the process of applying machine learning effectively to a variety of problems rather than emphasizing an understanding of the theory behind what makes machine learning work. This course does not assume any prior exposure to machine learning theory or practice.

We will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, we will cover topics such as decision trees, rule based classification, support vector machines, Bayesian networks, and clustering. In addition to readings from the course textbook, we will have additional readings from research articles that will be announced ahead of time and distributed on Blackboard.

Grades will be based on weekly assignments and quizzes, 2 take-home midterms, and a course project.

Assignments will include readings and experiments using the Weka toolkit and the TagHelper tools toolkit. Assignments will be distributed in class on Tuesday each week and will be due the following Tuesday before class. You will just get credit for doing these.

Quizzes will be given at the beginning of class each Tuesday. You will just get credit for doing these. These are meant to help you assess your level of understanding.

Take home mid-terms will be distributed at the end of class on a Tuesday or Thursday, and will be due 24 hours later.

The term project will involve applying machine learning to a substantial problem of the student.s choice. Several options are found in the Projects subfolder of the Course Documents folder on blackboard. Students may select one of these projects or may propose one of their own design. Students who wish to design their own project should check in about their plans with the instructor as early as possible in the semester.

If you have any questions, don't hesitate to Send me email!

Carolyn Penstein Rose ( Carnegie Mellon University