The Learning Problem (Analysis) 
The Learning Problem (Analysis)
by Caltech / Yaser Abu-Mostafa
Video Lecture 1 of 18
Copyright Information: Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND).
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Date Added: January 16, 2015

Lecture Description

Topics: Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Course Index

Course Description

This is an introductory course by Caltech Professor Yaser Abu-Mostafa on machine learning that covers the basic theory, algorithms, and applications. Machine learning (ML) enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML techniques are widely applied in engineering, science, finance, and commerce to build systems for which we do not have full mathematical specification (and that covers a lot of systems). The course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The course has 18 video lectures, each mainly focused on mathematical (theory), practical (technique), or conceptual (analysis) aspects. Check out the official course website for more information, including a very informative list of topics: https://work.caltech.edu/library/

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