Course: Machine Learning Course - CS 156 Dnatube

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Lec 1 - Lecture 01 - The Learning Proble ...

Lec 1 - Lecture 01 - The Learning Problem (April 3, 2012). The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 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...
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Lec 2 - Lecture 02 - Is Learning Feasibl ...

Lec 2 - Lecture 02 - Is Learning Feasible? (April 5, 2012). Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 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...
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Lec 3 - Lecture 03 -The Linear Model I ( ...

Lec 3 - Lecture 03 -The Linear Model I (April 10, 2012). The Linear Model I - Linear classification and linear regression. Extending linear models through nonlinear transforms. Lecture 3 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 -...
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Lec 4 - Lecture 04 - Error and Noise (Ap ...

Lec 4 - Lecture 04 - Error and Noise (April 12, 2012). Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy. Lecture 4 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 -...
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Lec 5 - Lecture 05 - Training Versus Tes ...

Lec 5 - Lecture 05 - Training Versus Testing (April 17, 2012). Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 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...
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Lec 6 - Lecture 06 - Theory of Generaliz ...

Lec 6 - Lecture 06 - Theory of Generalization (April 19, 2012). Theory of Generalization - How an infinite model can learn from a finite sample. The most important theoretical result in machine learning. Lecture 6 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...
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Lec 7 - Lecture 07 - The VC Dimension (A ...

Lec 7 - Lecture 07 - The VC Dimension (April 24, 2012). The VC Dimension - A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom. Lecture 7 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...
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Lec 8 - Lecture 08 - Bias-Variance Trade ...

Lec 8 - Lecture 08 - Bias-Variance Tradeoff (April 26, 2012). Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves. Lecture 8 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 -...
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Lec 9 - Lecture 09 - The Linear Model II ...

Lec 9 - Lecture 09 - The Linear Model II (May 1, 2012). The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent. Lecture 9 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 -...
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Lec 10 - Lecture 10 - Neural Networks (M ...

Lec 10 - Lecture 10 - Neural Networks (May 3, 2012). Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 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 -...
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Lec 11 - Lecture 11 - Overfitting (May 8 ...

Lec 11 - Lecture 11 - Overfitting (May 8, 2012). Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise. Lecture 11 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 -...
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Lec 12 - Lecture 12 - Regularization (Ma ...

Lec 12 - Lecture 12 - Regularization (May 10, 2012). Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay. Lecture 12 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 -...
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Lec 13 - Lecture 13 - Validation (May 15 ...

Lec 13 - Lecture 13 - Validation (May 15, 2012). Validation - Taking a peek out of sample. Model selection and data contamination. Cross validation. Lecture 13 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 -...
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Lec 14 - Lecture 14 - Support Vector Mac ...

Lec 14 - Lecture 14 - Support Vector Machines (May 17, 2012). Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 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...
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Lec 15 - Lecture 15 - Kernel Methods (Ma ...

Lec 15 - Lecture 15 - Kernel Methods (May 22, 2012). Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins. Lecture 15 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...
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Lec 16 - Lecture 16 - Radial Basis Funct ...

Lec 16 - Lecture 16 - Radial Basis Functions (May 24, 2012). Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 16 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 -...
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Lec 17 - Lecture 17 - Three Learning Pri ...

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...
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Lec 18 - Lecture 18 - Epilogue (May 31, ...

Lec 18 - Lecture 18 - Epilogue (May 31, 2012). Epilogue - The map of machine learning. Brief views of Bayesian learning and aggregation methods. Lecture 18 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 -...
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Lec 19 - Lecture 01 - The Learning Probl ...

Lec 19 - Lecture 01 - The Learning Problem (April 3, 2012) **NEW AUDIO. This lecture is the same one as http://youtu.be/VeKeFIepJBU but with correct audio dropouts. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View...

Machine Learning Course - CS 156


Source of these courses is California Inst. of Technology 
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 balan
California Inst. of Technology  Website: http://www.dnatube.com/school/caltech

COURSE NAME: Machine Learning Course - CS 156

33890 LECTURE VIEWS

2917 COURSE VIEWS