6.862 Applied Machine Learning

Note: This is the website for Spring 2017. In Spring 2018, this class is taught by Leslie Kaelbling and Tomas Lozano-Perez. If you are interested in taking the class in 2018, please pre-register for it, and visit http:introml.mit.edu/.

Spring 2017

This class is the graduate version of 6.036 Introduction to Machine Learning. Quoting from the description of 6.036:

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.

The graduate version (6.862) coincides with 6.036 in lectures, problem sets, and exams. In addition, 6.862 includes a semester-long class project and office hour like meetings to discuss the project. This website here is mainly concerned with the project part.

Logistics and more

If you want to take this class:

If you are an undergraduate student or EECS graduate student, you may still take 6.036 or 6.867.


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