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:
Lecture 1 - The Motivation & Applications of Machine Learning
May 18, 2009
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31135 Views
Lecture 2 - An Application of Supervised Learning - Autonomous Deriving
May 18, 2009
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Lecture 3 - The Concept of Underfitting and Overfitting
May 18, 2009
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Lecture 4 - Newton's Method
May 18, 2009
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Lecture 5 - Discriminative Algorithms
May 18, 2009
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Lecture 6 - Multinomial Event Model
May 18, 2009
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6146 Views
Lecture 7 - Optimal Margin Classifier
May 18, 2009
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Lecture 8 - Kernels, Mercer's Theorem...
May 18, 2009
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Lecture 9 - Bias/variance Tradeoff
May 18, 2009
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Lecture 10 - Uniform Convergence - The Case of Infinite H
May 18, 2009
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Lecture 11 - Bayesian Statistics and Regularization
May 18, 2009
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Lecture 12 - The Concept of Unsupervised Learning
May 18, 2009
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Lecture 13 - Mixture of Gaussian
May 18, 2009
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Lecture 14 - The Factor Analysis Model
May 18, 2009
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Lecture 15 - Latent Semantic Indexing (LSI)
May 18, 2009
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Lecture 16 - Applications of Reinforcement Learning
May 18, 2009
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Lecture 17 - Generalization to Continuous States
May 18, 2009
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Lecture 18 - State-action Rewards
May 18, 2009
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Lecture 19 - Advice for Applying Machine Learning
May 18, 2009
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Lecture 20 - Partially Observable MDPs (POMDPs)
May 18, 2009
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5671 Views