Research > Statistics & Machine Learning
Overview
How can a computer learn to diagnose cancer? How can a robotic assistant learn to adapt to the specific habits of their owners? Machine learning is the study of how computers can learn complex concepts from data and experience, and seeks to answer the fundamental research questions underpinning the challenges outlined above.
Machine learning is a core area in CMS, and has strong connections to virtually all areas of the information sciences. We are particularly interested in pursuing emerging connections between machine learning and other disciplines, which we believe will stimulate the development of the next generation of machine learning methods and technologies.
Faculty
Aaron Ames, Yaser Abu-Mostafa, Venkat Chandrasekaran, Frederick Eberhardt, Katrina Ligett, Lior Patcher, Pietro Perona, Andrew Stuart, Joel Tropp, Yisong Yue
Related research groups & Centers > CDS, CD3, Center for Data Science and Technology, CMI, CNS, DOLCIT, RSRG
Recent Research Talks
Visipedia – A distributed visual system composed of machines and people - Pietro Perona 2/26/14
Computational and Statistical Tradeoffs via Convex Relaxation - Venkat Chandrasekaran 9/27/13
Machine Learning Course - CS 156 - Yaser Abu-Mostafa 8/28/12
Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem - Yisong Yue 2009
Related Courses
ME/CS 131. Advanced robotics: Manipulation and sensing.
ME/CS 132 ab. Advanced robotics: Navigation and vision.
EE/CNS/CS 148ab. Selected topics in computational vision.
BEM/Ec 150 Business Analytics.
CS/CNS/EE 155. Machine learning and data mining.
CS/CNS/EE 156ab. Learning systems.
CS/CNS/EE 159. Projects in machine learning and AI.
CNS/Bi/EE/CS/NB 186. Vision: From computational theory to neuronal mechanisms.
ACM/CS/EE 218. Statistical inference.
CS/CNS/EE 253. Special topics in machine learning.
CNS/Bi/Ph/CS/NB 187. Neural computation.