Stanford Engineering Everywhere CS229 - Machine Learning

Stanford Engineering Everywhere CS229 - Machine Learning

20 Lectures · Jul 22, 2008

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

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Uploaded videos:

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01:08:39

Lecture 1 - The Motivation & Applications of Machine Learning

Andrew Ng

May 18, 2009

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31135 Views

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01:16:13

Lecture 2 - An Application of Supervised Learning - Autonomous Deriving

Andrew Ng

May 18, 2009

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01:13:11

Lecture 3 - The Concept of Underfitting and Overfitting

Andrew Ng

May 18, 2009

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01:13:03

Lecture 4 - Newton's Method

Andrew Ng

May 18, 2009

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01:15:29

Lecture 5 - Discriminative Algorithms

Andrew Ng

May 18, 2009

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01:13:07

Lecture 6 - Multinomial Event Model

Andrew Ng

May 18, 2009

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01:15:41

Lecture 7 - Optimal Margin Classifier

Andrew Ng

May 18, 2009

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01:17:16

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

Andrew Ng

May 18, 2009

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01:14:15

Lecture 9 - Bias/variance Tradeoff

Andrew Ng

May 18, 2009

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01:12:54

Lecture 10 - Uniform Convergence - The Case of Infinite H

Andrew Ng

May 18, 2009

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01:22:16

Lecture 11 - Bayesian Statistics and Regularization

Andrew Ng

May 18, 2009

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01:14:19

Lecture 12 - The Concept of Unsupervised Learning

Andrew Ng

May 18, 2009

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01:14:54

Lecture 13 - Mixture of Gaussian

Andrew Ng

May 18, 2009

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01:20:37

Lecture 14 - The Factor Analysis Model

Andrew Ng

May 18, 2009

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01:17:15

Lecture 15 - Latent Semantic Indexing (LSI)

Andrew Ng

May 18, 2009

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01:13:03

Lecture 16 - Applications of Reinforcement Learning

Andrew Ng

May 18, 2009

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01:16:59

Lecture 17 - Generalization to Continuous States

Andrew Ng

May 18, 2009

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01:16:37

Lecture 18 - State-action Rewards

Andrew Ng

May 18, 2009

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01:15:52

Lecture 19 - Advice for Applying Machine Learning

Andrew Ng

May 18, 2009

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5125 Views

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Lecture 20 - Partially Observable MDPs (POMDPs)

Andrew Ng

May 18, 2009

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5671 Views

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