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Andrew Ng Assistant Professor, Department of Computer Science Stanford

Andrew Ng

Assistant Professor, Department of Computer Science
Stanford

Courses

  1. Machine Learning Course

    Machine Learning

    Stanford / Computer Science
    Andrew Ng

Lectures

  1. The Motivation & Applications of Machine Learning Lecture
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    The Motivation & Applications of Machine Learning

    Stanford / Computer Science
    Andrew Ng
  2. State-action Rewards Lecture
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    State-action Rewards

    Stanford / Computer Science
    Andrew Ng
  3. The Factor Analysis Model Lecture
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    The Factor Analysis Model

    Stanford / Computer Science
    Andrew Ng
  4. Latent Semantic Indexing (LSI) Lecture
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    Latent Semantic Indexing (LSI)

    Stanford / Computer Science
    Andrew Ng
  5. Generalization to Continuous States Lecture
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    Generalization to Continuous States

    Stanford / Computer Science
    Andrew Ng
  6. An Application of Supervised Learning - Autonomous Deriving Lecture
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    An Application of Supervised Learning - Autonomous Deriving

    Stanford / Computer Science
    Andrew Ng
  7. The Concept of Underfitting and Overfitting Lecture
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    The Concept of Underfitting and Overfitting

    Stanford / Computer Science
    Andrew Ng
  8. Newton's Method Lecture
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    Newton's Method

    Stanford / Computer Science
    Andrew Ng
  9. Discriminative Algorithms Lecture
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    Discriminative Algorithms

    Stanford / Computer Science
    Andrew Ng
  10. Multinomial Event Model Lecture
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    Multinomial Event Model

    Stanford / Computer Science
    Andrew Ng
  11. Optimal Margin Classifier Lecture
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    Optimal Margin Classifier

    Stanford / Computer Science
    Andrew Ng
  12. Kernels Lecture
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    Kernels

    Stanford / Computer Science
    Andrew Ng
  13. Bias/variance Tradeoff Lecture
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    Bias/variance Tradeoff

    Stanford / Computer Science
    Andrew Ng
  14. Uniform Convergence - The Case of Infinite H Lecture
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    Uniform Convergence - The Case of Infinite H

    Stanford / Computer Science
    Andrew Ng
  15. Bayesian Statistics and Regularization Lecture
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    Bayesian Statistics and Regularization

    Stanford / Computer Science
    Andrew Ng
  16. The Concept of Unsupervised Learning Lecture
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    The Concept of Unsupervised Learning

    Stanford / Computer Science
    Andrew Ng
  17. Mixture of Gaussian Lecture
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    Mixture of Gaussian

    Stanford / Computer Science
    Andrew Ng
  18. Partially Observable MDPs (POMDPs) Lecture
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    Partially Observable MDPs (POMDPs)

    Stanford / Computer Science
    Andrew Ng
  19. Applications of Reinforcement Learning Lecture
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    Applications of Reinforcement Learning

    Stanford / Computer Science
    Andrew Ng
  20. Advice for Applying Machine Learning Lecture
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    Advice for Applying Machine Learning

    Stanford / Computer Science
    Andrew Ng

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    Competitive Analysis

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  2. Performance Lecture

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  3. Introduction to LISP Lecture

    Introduction to LISP

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  4. Performance Lecture

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