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CS 156: Machine Learning Course (Spring 2012, Caltech). This is an introductory course by Caltech Professor Yaser Abu-Mostafa
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English [CC]
FREE
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Description
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.
Course content
- Lecture 01 – The Learning Problem Unlimited
- Lecture 02 – Is Learning Feasible? Unlimited
- Lecture 03 – The Linear Model I Unlimited
- Lecture 04 – Error and Noise Unlimited
- Lecture 05 – Training Versus Testing Unlimited
- Lecture 06 – Theory of Generalization Unlimited
- Lecture 07 – The VC Dimension Unlimited
- Lecture 08 – Bias-Variance Tradeoff Unlimited
- Lecture 09 – The Linear Model II Unlimited
- Lecture 10 – Neural Networks Unlimited
- Lecture 11 – Overfitting Unlimited
- Lecture 12 – Regularization Unlimited
- Lecture 13 – Validation Unlimited
- Lecture 14 – Support Vector Machines Unlimited
- Lecture 15 – Kernel Methods Unlimited
- Lecture 16 – Radial Basis Functions Unlimited
- Lecture 17 – Three Learning Principles Unlimited
- Lecture 18 – Epilogue Unlimited
Instructor
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