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Course

# Machine Learning

California Institute 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 balances theory and practice, and covers the mathematical as well as the heuristic aspects.

Home > Computer Science > Artificial Intelligence > Machine LearningLectures:
• ### The Learning Problem

01:21:33Yaser Abu-Mostafa

The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem.

• ### Error and Noise

01:18:22Yaser Abu-Mostafa

Error and Noise - The principled choice of error measures. What happens when the target we want to learn is noisy.

• ### Training Versus Testing

01:16:58Yaser Abu-Mostafa

Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize?

• ### Theory of Generalization

01:18:12Yaser Abu-Mostafa

Theory of Generalization - How an infinite model can learn from a finite sample. The most important theoretical result in machine learning.

• ### The VC Dimension

01:13:31Yaser Abu-Mostafa

The VC Dimension - A measure of what it takes a model to learn. Relationship to the number of parameters and degrees of freedom.

01:16:51Yaser Abu-Mostafa

Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves.

• ### The Linear Model II

01:27:14Yaser Abu-Mostafa

The Linear Model II - More about linear models. Logistic regression, maximum likelihood, and gradient descent.

• ### Neural Networks

01:25:16Yaser Abu-Mostafa

Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers.

• ### Overfitting

01:19:49Yaser Abu-Mostafa

Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise.

• ### Regularization

01:15:14Yaser Abu-Mostafa

Regularization - Putting the brakes on fitting the noise. Hard and soft constraints. Augmented error and weight decay.

• ### Validation

01:26:12Yaser Abu-Mostafa

Validation - Taking a peek out of sample. Model selection and data contamination. Cross validation.

• ### Support Vector Machines

01:14:16Yaser Abu-Mostafa

Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one.

• ### Kernel Methods

01:18:19Yaser Abu-Mostafa

Kernel Methods - Extending SVM to infinite-dimensional spaces using the kernel trick, and to non-separable data using soft margins.

01:22:08Yaser Abu-Mostafa

Radial Basis Functions - An important learning model that connects several machine learning models and techniques.

• ### Three Learning Principles

01:16:18Yaser Abu-Mostafa

Three Learning Principles - Major pitfalls for machine learning practitioners; Occam's razor, sampling bias, and data snooping.

• ### Epilogue

01:09:28Yaser Abu-Mostafa

Epilogue - The map of machine learning. Brief views of Bayesian learning and aggregation methods.