Lecture: Lec 17 - Lecture 17 - Three Learning Principles (May 29, 2012) - DnaTube.com - Scientific Video and Animation Site

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Lec 17 - Lecture 17 - Three Learning Principles (May 29, 2012). Three Learning Principles - Major pitfalls for machine learning practitioners; Occam's razor, sampling bias, and data snooping. Lecture 17 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunesu.itunes.apple.com/audit/CODBABB3ZC and on the course website - http://work.caltech.edu/telecourse.html

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Uploaded by: caltechmachine ( Send Message ) on 28-08-2012.

Duration: 76m 18s

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This video is a part of a lecture series from of California Inst. of Technology

Lecture list for this course

Lec 1 - Lecture 01 - The Learning Problem (April 3, 2012)

Lec 2 - Lecture 02 - Is Learning Feasible? (April 5, 2012)

Lec 3 - Lecture 03 -The Linear Model I (April 10, 2012)

Lec 4 - Lecture 04 - Error and Noise (April 12, 2012)

Lec 5 - Lecture 05 - Training Versus Testing (April 17, 2012)

Lec 6 - Lecture 06 - Theory of Generalization (April 19, 2012)

Lec 7 - Lecture 07 - The VC Dimension (April 24, 2012)

Lec 8 - Lecture 08 - Bias-Variance Tradeoff (April 26, 2012)

Lec 9 - Lecture 09 - The Linear Model II (May 1, 2012)

Lec 10 - Lecture 10 - Neural Networks (May 3, 2012)

Lec 11 - Lecture 11 - Overfitting (May 8, 2012)

Lec 12 - Lecture 12 - Regularization (May 10, 2012)

Lec 13 - Lecture 13 - Validation (May 15, 2012)

Lec 14 - Lecture 14 - Support Vector Machines (May 17, 2012)

Lec 15 - Lecture 15 - Kernel Methods (May 22, 2012)

Lec 16 - Lecture 16 - Radial Basis Functions (May 24, 2012)

Lec 18 - Lecture 18 - Epilogue (May 31, 2012)

Lec 19 - Lecture 01 - The Learning Problem (April 3, 2012) **NEW AUDIO