Class lectures: Mondays and Wednesdays 10:30-11:50 in Newell Simon Hall 1305

Recitations: Wednesday, 6:00-8:00 pm GHC 8102

Homework 5 is due on Wed, Dec 2nd.

Fri, Dec. 4, 1-4pm GHC 7th floor (under staircase)

Homework 4 solutions are posted

Machine Learning is concerned with computer programs that learn to make better predictions or take better actions given increasing numbers of observations (e.g., programs that learn to spot high-risk medical patients, recognize human faces, recommend music and movies, or drive autonomous robots). This course covers theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, boosting, support-vector machines, dimensionality reduction, and reinforcement learning. The course also covers theoretical concepts such as bias-variance trade-off, PAC learning, margin-based generalization bounds, and Occam's Razor. Short programming assignments include hands-on experiments with various learning algorithms. Typical assignments include learning to automatically classify email by topic, and learning to automatically classify the mental state of a person from brain image data. The course will include a term project where the students will have opportunity to explore some of the class topics on a real-world data set in more detail.

Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. This class is intended for Masters students and advanced undergraduates.

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Homework policy

Important Note: As we sometimes reuse problem set questions from previous years, or problems covered by papers and web pages, we expect the students will not copy, refer to, or look at the solutions in preparing their answers. Since this is an upper level course, we hope you want to learn and not Google for answers. The purpose of problem sets is to help you think about the material, not just give us the right answers. Therefore, please restrict attention to the books mentioned on the web page when solving problem sets. If you do happen to use other material, acknowledge it clearly with a citation on the submitted solution.

Collaboration policy

Homeworks will be done individually: each student must hand in their own answers. In addition, each student must write their own code in the programming part of the assignment. It is acceptable, however, for students to collaborate on general solution strategies. We assume that, as participants of an upper level course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. To help ensure that you understand every solution, you may take notes during study sessions, but in preparing your own writeup, you must close your notes and not refer to any written materials from a joint study session. Indicate on each homework with whom you collaborated. The final project may be completed in teams of 2-3 students.

Late homework policy

Homework regrading policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to Michelle, and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

Final project

The course project will account for the 25% of the final grade, the following will contribute to the project grade:

For project milestone, roughly half of the project work should be completed. A short write-up will be required, and we will provide feedback.