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This is an introductory course 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. Detailed topics are listed below.

- Lecture 1:
**The Learning Problem***(Tuesday, April 3)* - Lecture 2:
**Is Learning Feasible?***(Thursday, April 5)* - Lecture 3:
**The Linear Model I***(Tuesday, April 10)* - Lecture 4:
**Error and Noise***(Thursday, April 12)* - Lecture 5:
**Training versus Testing***(Tuesday, April 17)* - Lecture 6:
**Theory of Generalization***(Thursday, April 19)* - Lecture 7:
**The VC Dimension***(Tuesday, April 24)* - Lecture 8:
**Bias-Variance Tradeoff***(Thursday, April 26)* - Lecture 9:
**The Linear Model II***(Tuesday, May 1)* - Lecture 10:
**Neural Networks***(Thursday, May 3)* - Lecture 11:
**Overfitting***(Tuesday, May 8)* - Lecture 12:
**Regularization***(Thursday, May 10)* - Lecture 13:
**Validation***(Tuesday, May 15)* - Lecture 14:
**Support Vector Machines***(Thursday, May 17)* - Lecture 15:
**Kernel Methods***(Tuesday, May 22)* - Lecture 16:
**Radial Basis Functions***(Thursday, May 24)* - Lecture 17:
**Three Learning Principles***(Tuesday, May 29)* - Lecture 18:
**Epilogue***(Thursday, May 31)*

technique; practical

analysis; conceptual

**10:30 AM**PDT (US West Coast)**1:30 PM**EDT (US East Coast)**2:30 PM**in Brazil and Argentina**6:30 PM**in the UK**7:30 PM**in middle Europe and Egypt**9:30 PM**in Western Russia and the Emirates**11:00 PM**in India- After midnight (following day) in the far east and Australia

View details at http://work.caltech.edu/telecourse.html

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