Introduction to Machine Learning

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/

Copyright Information

Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND).
Introduction to Machine Learning
Scatterplot featuring a linear support vector machine's decision boundary (dashed line) (Source: wikipedia.org)
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Video Lectures & Study Materials

Visit the official course website for more study materials: https://work.caltech.edu/telecourse.html

# Lecture Play Lecture
1 The Learning Problem (Analysis) (1:21:28) Play Video
1 The Learning Problem (Analysis) (1:21:28) Play Video
2 Is Learning Feasible? (Theory) (1:16:49) Play Video
2 Is Learning Feasible? (Theory) (1:16:49) Play Video
3 The Linear Model (Technique) (1:19:44) Play Video
3 The Linear Model (Technique) (1:19:44) Play Video
4 Error Measures and Noise (Analysis) (1:18:22) Play Video
4 Error Measures and Noise (Analysis) (1:18:22) Play Video
5 Training versus Testing (Theory) (1:16:58) Play Video
5 Training versus Testing (Theory) (1:16:58) Play Video
6 Theory of Generalization (1:18:12) Play Video
6 Theory of Generalization (1:18:12) Play Video
7 The VC Dimension (Theory) (1:13:31) Play Video
7 The VC Dimension (Theory) (1:13:31) Play Video
8 Bias-Variance Tradeoff (Theory) (1:16:51) Play Video
8 Bias-Variance Tradeoff (Theory) (1:16:51) Play Video
9 The Linear Model II (Technique) (1:27:14) Play Video
9 The Linear Model II (Technique) (1:27:14) Play Video
10 Neural Networks (Technique) (1:25:16) Play Video
10 Neural Networks (Technique) (1:25:16) Play Video
11 Overfitting (Analysis) (1:19:49) Play Video
11 Overfitting (Analysis) (1:19:49) Play Video
12 Technique: Regularization (1:15:14) Play Video
12 Technique: Regularization (1:15:14) Play Video
13 Technique: Validation (1:26:12) Play Video
13 Technique: Validation (1:26:12) Play Video
14 Support Vector Machines (Theory and Technique) (1:14:16) Play Video
14 Support Vector Machines (Theory and Technique) (1:14:16) Play Video
15 Kernel Methods (Theory and Technique) (1:18:19) Play Video
15 Kernel Methods (Theory and Technique) (1:18:19) Play Video
16 Radial Basis Functions (Technique) (1:22:08) Play Video
16 Radial Basis Functions (Technique) (1:22:08) Play Video
17 Three Learning Principles (Analysis) (1:16:18) Play Video
17 Three Learning Principles (Analysis) (1:16:18) Play Video
18 Epilogue: The Map of Machine Learning (Analysis) (1:09:28) Play Video
18 Epilogue: The Map of Machine Learning (Analysis) (1:09:28) Play Video

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