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Learning From Data
2
Instructor: Yaser S. Abu-Mostafa
Department: Electrical Engineering and Computer Science
Institution: California Institute of Technology
Platform: Independent
Year: 2012
Price: Free
Prerequisites: probability, matrices, calculus
Textbook:
Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin. Learning From Data. 2012.
Description:
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion: 1. What is learning? 2. Can a machine learn? 3. How to do it? 4. How to do it well? 5. Take-home lessons.