Online course from MIT Open Learning Library
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Table of Contents
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
- Understand the formulation of well-specified machine learning problems.
- Learn how to perform supervised and reinforcement learning, with images and temporal sequences.
Leslie Pack Kaelbling is Professor of Computer Science and Engineering at MIT. She has previously held positions at Brown University, the Artificial Intelligence Center of SRI International, and at Teleos Research. In 2000, she founded the Journal of Machine Learning Research, a high-quality journal that is both freely available electronically as well as published in archival form; she currently serves as editor-in-chief.
Tomas Lozano-Perez is currently the School of Engineering Professor in Teaching Excellence at the Massachusetts Institute of Technology (MIT), USA, where he is a member of the Computer Science and Artificial Intelligence Laboratory. He has been Associate Director of the Artificial Intelligence Laboratory and Associate Head for Computer Science of MIT's Department of Electrical Engineering and Computer Science.
Isaac Chuang is Senior Associate Dean of Digital Learning, and Professor of Electrical Engineering & Computer Science, and Professor of Physics, at the Massachusetts Institute of Technology. He is associate director of the MIT Office of Digital Learning, and leads the quanta research group at the Center for Ultracold Atoms, in the MIT Research Laboratory of Electronics.
Dr. Duane S. Boning is the Clarence J. LeBel Professor in Electrical Engineering, and Professor of Electrical Engineering and Computer Science in the EECS Department at MIT. He is affiliated with the MIT Microsystems Technology Laboratories, and serves as MTL Associate Director for Computation and CAD. He is also the Engineering Faculty Co-Director of the MIT Leaders for Global Operations (LGO) program.
This course includes lectures, lecture notes, exercises, labs, and homework problems.
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