6.036  Intro to Machine Learning

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6.036  Intro to Machine Learning

Spring 2014

Instructors: Regina Barzilay, Tommi S Jaakkola, George Dimitri Konidaris, Jacob K White

TAs: Shervin Mehryar, Eric Shyu, Vadim S Smolyakov, Arvind Thiagarajan, Li Yu

Lecture:  TR9.30-11  (10-250)
Recitations:  F11-12, F12-1, F1-2, F2-3,F3-4  (36-156, 36-156, 4-261, 4-261, 4-261 )
Office Hours:  M4-5:30, T4-5:30, W6-7:30, R4-5:30, R6-7:30  (36-144, 36-153, 36-155, 38-166, 38-166)    

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.

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