6.036  Intro to Machine Learning

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6.036  Intro to Machine Learning

Spring 2015

Instructors: Regina Barzilay, Tommi S Jaakkola, Suvrit Sra

TAs: David Alvarez Melis, Nirav Bhan, Zoya Bylinskii, Elisa Castaner, Jeffrey D Chan, Brando Miranda, He Sun, Alex C Wang

Lecture:  TR9.30-11  (10-250)        

Information: 

Please see the course information sheet for information about problem sets, exams, and policies. For information beyond the sheet, you can reach the staff at 6036-staff@lists.csail.mit.edu.

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.

Recitation Timings and Venue:

R01    32-141    F11-12
R02    1-190    F12-1
R03    35-225    F1-2
R04    32-141    F2-3
R05    4-270    F3-4
R06    4-163    F4-5

Notice normal recitation room for R04 was changed 32-141 with 32-155.

Office Hours Timings:

Elisa Castaner
Mondays 6:30-8pm
26-328

Zoya Bylinskii
Tuesdays 4-5:30pm
34-304

David Alvarez Melis
Wednesdays 5:00-6:30pm
26-328

Alex Wang
Thursdays 4-5:30pm
34-304

Jeffrey Chan
Wednesdays 1-2:30pm
26-168

He Sun
Mondays 4:30-6:00pm
34-304

Nirav Bhan
Wednesdays 3.30-5pm
34-303

Brando Miranda
Tuesdays 5.30-7:00pm
34-301

Announcements