Machine Learning

Andrew Ng
  • 3.9 (160)
  • TECHNOLOGY
Machine Learning

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. 07/23/2008 · VIDEO

    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 sec

  2. 07/23/2008 · VIDEO

    2. Machine Learning Lecture 2

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

    4 sec

  3. 07/23/2008 · VIDEO

    3. Machine Learning Lecture 3

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

    4 sec

  4. 07/23/2008 · VIDEO

    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 sec

  5. 07/23/2008 · VIDEO

    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 sec

  6. 07/23/2008 · VIDEO

    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 sec

  7. 07/23/2008 · VIDEO

    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 sec

  8. 07/23/2008 · VIDEO

    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 sec
See All (20)

3.9
out of 5
160 Ratings

About

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.

Information

More From Stanford

To listen to explicit episodes, sign in.

Stay up to date with this show

Sign in or sign up to follow shows, save episodes, and get the latest updates.
Select a country or region

Africa, Middle East, and India

Asia Pacific

Europe

Latin America and the Caribbean

The United States and Canada
OSZAR »