6.036 Introduction to Machine Learning (Spring 2015)
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 (firstname.lastname@example.org):
(please read this page carefully before contacting the staff)
- Tuesdays and Thursdays, 9:30am--11am (10-250)
- Profs. Regina Barzilay, Tommi Jaakkola, Jacob White
- Weekly recitations/tutorials on Fridays
F11-12 (32-141), F12-1 (1-190),
Exams (60% of grade):
- Midterm on March 19, closed book, 1.5h, in-class, 20% of grade.
have a conflict with the exam time, please
notify us of your midterm
conflict at least two weeks before the exam.
- Final exam on May 19, 1:30-4:30pm, (Johnson Ice Rink), closed book, 3h, 40% of
Any conflicts with the final exam time are handled by the registrar's office.
Assignments (40% of grade in total):
- Regular homework assignments (10% of the grade in total).
- Three projects, each worth 10% of the grade, involving programming in python/MATLAB.
- Late submission policy for homeworks and projects:
Assignments are due 9am on specified Fridays. Solutions will be
posted immediately after the deadline. Late assignments are NOT
accepted. If you have a valid reason (= dean's note),
you must notify
us via email EMAIL
VALID REASON. For students with a valid reason in this sense, the portion of
the grade allocated to the missed assignment will be added to the final
exam weight. Missed assignment without a valid reason means
getting the score zero on the assignment.
- 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.
- Handed out and solved during lectures, not graded.