Machine Learning

Overview 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.

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Created by Admin corner
Last updated Wed, 08-Jun-2022
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Course overview
Lecture Details

The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltechs Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html

Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/

This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Curriculum for this course
18 Lessons 23:36:05 Hours
Lecture
18 Lessons 23:36:05 Hours
  • The Learning Problem
    Preview 01:21:28
  • Is Learning Feasible?
    01:16:49
  • The Linear Model I
    01:19:44
  • Error and Noise
    01:18:22
  • Training Versus Testing
    01:16:58
  • Theory of Generalization
    01:18:12
  • The VC Dimension
    01:13:30
  • Bias-Variance Tradeoff
    01:16:51
  • The Linear Model II
    01:27:14
  • Neural Networks
    01:25:16
  • Overfitting
    01:19:48
  • Regularization
    01:15:13
  • Validation
    01:26:12
  • Support Vector Machines
    01:14:16
  • Kernel Methods
    01:18:19
  • Radial Basis Functions
    01:22:08
  • Three Learning Principles
    01:16:18
  • Epilogue
    01:09:27
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