6.036  Introduction to Machine Learning

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6.036  Introduction to Machine Learning

Spring 2016

Instructors: Regina Barzilay, Tommi S Jaakkola, Suvrit Sra

TAs: Polina Binder, Benjamin S Greenberg, David Harwath, Nicholas I Hynes, Esther Han Beol Jang, Dongyoung Kim, Thanard Kurutach, Jonas Weylin Mueller, David N Reshef, Tuan M Tran, Amruth Venkatraman, Angel Yu, Haoyang Zeng

Lecture:  TR2.30-4  (26-100)        

Information: 

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, probabilistic modeling, reinforcement learning; and methods such as on-line algorithms, support vector machines, neural networks/deep learning, hidden Markov models, and Bayesian networks.

Please check the course information sheet .

Office Hours: Mon 1-3 pm 34-302, 5-7 pm 34-304; Tue 12-2 pm 26-314, 4-8 pm 34-304; Wed 4-6 pm 34-302; Thu 11 am-1 pm 26-314, 4-8 pm 36-112

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