Machine Learning | caltech
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Course Details
- Course Lessons18
- Course Period23h 36m
- No.Students1
- LanguageEnglish
- No Prerequisite
- (1)
- Start Now for free
Course Lessons
- 1 | Lecture 01 - The Learning Problem 01:21:28
- 2 | Lecture 02 - Is Learning Feasible? 01:16:49
- 3 | Lecture 03 -The Linear Model I 01:19:44
- 4 | Lecture 04 - Error and Noise 01:18:22
- 5 | Lecture 05 - Training Versus Testing 01:16:58
- 6 | Lecture 06 - Theory of Generalization 01:18:12
- 7 | Lecture 07 - The VC Dimension 01:13:31
- 8 | Lecture 08 - Bias-Variance Tradeoff 01:16:51
- 9 | Lecture 09 - The Linear Model II 01:27:14
- 10 | Lecture 10 - Neural Networks 01:25:16
- 11 | Lecture 11 - Overfitting 01:19:49
- 12 | Lecture 12 - Regularization 01:15:14
- 13 | Lecture 13 - Validation 01:26:12
- 14 | Lecture 14 - Support Vector Machines 01:14:16
- 15 | Lecture 15 - Kernel Methods 01:18:19
- 16 | Lecture 16 - Radial Basis Functions 01:22:08
- 17 | Lecture 17 - Three Learning Principles 01:16:18
- 18 | Lecture 18 - Epilogue 01:09:28
Student Reviews
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1 reviewYoutube
29-07-2024
Machine Learning Course - CS 156
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.
Find course materials in the iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the CS156 website - http://work.caltech.edu/telecourse.html