6.036 Introduction to Machine Learning

6.036 Introduction to Machine Learning (Spring 2014)

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover concepts such as representation, over-fitting, regularization, and generalization; topics such as clustering, classification, recommender problems, and probabilistic modeling; and methods such as on-line algorithms, support vector machines, hidden Markov models, and Bayesian networks.

Teaching staff (6036-staff@lists.csail.mit.edu):

Instructors: Prof. Regina Barzilay, Prof. Tommi Jaakkola, Dr. George Konidaris, Prof. Jacob White
TAs: Shervin Mehryar, Eric Shyu, Vadim S Smolyakov, Arvind Thiagarajan, Li Yu

Lectures (tentative schedule):

Tuesdays and Thursdays, 9:30am--11am (10-250)
Profs. Regina Barzilay, Tommi Jaakkola

Recitations:

Weekly recitations/tutorials on Fridays (choose one)
F11-12 (36-156), F12-1 (36-156), F1-2 (4-261), F2-3(4-261), F3-4(4-261)

Exams (60% of grade):

Midterm on March 20, closed book, 1.5h, in-class, 20% of grade.

notify us of your midterm conflict

two weeks before

Final exam during finals week, closed book, 3h, 40% of grade.

Problem sets (35% of grade):

Bi-weekly homework assignments (always due Fridays 9am).
Three projects that involve programming in python/MATLAB.

Late submission policy for homeworks and projects:

You can be up to three (3) days late, automatically losing 10% for each starting 24h (1min late means 10% off, 25h late means 20% off, and so on). The solutions will be posted exactly three days after the due date, Mondays at 9am. No submissions will be accepted after the three days unless accompanied with a note from the dean.

If you have a valid reason (dean's note), you must notify us via valid late submission notification.

Collaboration policy for homeworks and projects:

We encourage students to discuss assignments with other students and with the teaching staff to better understand the concepts. However, when you submit an assignment under your name, we assume that you are certifying that the details are entirely your own work and that you played at least a substantial role in the conception stage.

You should not use results (solutions, code) from other students (from this year or previous years) in preparing yours to any assignment unless you developed the materials while working with other studens in class. Students should never share your solutions (or staff solutions) with other students.

In-class problems (5% of grade):

Handed out and solved during lectures, graded for effort. You are encouraged to discuss and work on these problems together with others at lecture.