#### About

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Students are expected to have the following background:

Prerequisites:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory. (Stat 116 is sufficient but not necessary.)
- Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

**Course Homepage:** SEE CS229 - Machine Learning (Fall,2007)

**Course features at Stanford Engineering Everywhere page:**
*Machine Learning
*Lectures
*Syllabus
*Handouts
*Assignments
*Resources

#### Related categories

## Uploaded videos:

### Lecture 1 - The Motivation & Applications of Machine Learning

May 18, 2009

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31129 Views

### Lecture 2 - An Application of Supervised Learning - Autonomous Deriving

May 18, 2009

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12034 Views

### Lecture 3 - The Concept of Underfitting and Overfitting

May 18, 2009

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12129 Views

### Lecture 4 - Newton's Method

May 18, 2009

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7074 Views

### Lecture 5 - Discriminative Algorithms

May 18, 2009

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7439 Views

### Lecture 6 - Multinomial Event Model

May 18, 2009

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6145 Views

### Lecture 7 - Optimal Margin Classifier

May 18, 2009

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5905 Views

### Lecture 8 - Kernels, Mercer's Theorem...

May 18, 2009

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10346 Views

### Lecture 9 - Bias/variance Tradeoff

May 18, 2009

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8453 Views

### Lecture 10 - Uniform Convergence - The Case of Infinite H

May 18, 2009

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4179 Views

### Lecture 11 - Bayesian Statistics and Regularization

May 18, 2009

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12280 Views

### Lecture 12 - The Concept of Unsupervised Learning

May 18, 2009

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6706 Views

### Lecture 13 - Mixture of Gaussian

May 18, 2009

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7969 Views

### Lecture 14 - The Factor Analysis Model

May 18, 2009

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12383 Views

### Lecture 15 - Latent Semantic Indexing (LSI)

May 18, 2009

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8164 Views

### Lecture 16 - Applications of Reinforcement Learning

May 18, 2009

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5959 Views

### Lecture 17 - Generalization to Continuous States

May 18, 2009

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3606 Views

### Lecture 18 - State-action Rewards

May 18, 2009

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3512 Views

### Lecture 19 - Advice for Applying Machine Learning

May 18, 2009

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5124 Views

### Lecture 20 - Partially Observable MDPs (POMDPs)

May 18, 2009

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5670 Views