Machine Learning

Andrew Ng

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. 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.

  1. 2008/07/23 · ビデオ

    1. Machine Learning Lecture 1

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

    4秒

  2. 2008/07/23 · ビデオ

    2. Machine Learning Lecture 2

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.

    4秒

  3. 2008/07/23 · ビデオ

    3. Machine Learning Lecture 3

    science, math, engineering, computer, technology, robotics, algebra, locally, weighted, logistic, regression, linear, probabilistic, interpretation, Gaussian, distribution, digression, perceptron

    4秒

  4. 2008/07/23 · ビデオ

    4. Machine Learning Lecture 4

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning.

    4秒

  5. 2008/07/23 · ビデオ

    5. Machine Learning Lecture 5

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning.

    4秒

  6. 2008/07/23 · ビデオ

    6. Machine Learning Lecture 6

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine.

    4秒

  7. 2008/07/23 · ビデオ

    7. Machine Learning Lecture 7

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals.

    4秒

  8. 2008/07/23 · ビデオ

    8. Machine Learning Lecture 8

    Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

    4秒
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評価とレビュー

4.5
5段階評価中
4件の評価

番組について

This course provides a broad introduction to machine learning and statistical pattern recognition. The course also discusses recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. 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.

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