Machine Learning podcast artwork
Technology

Machine Learning

by Andrew Ng
Share this ranking Claiming needs an RSS feed. Contact us.
Listen on Apple Podcasts

Where it charts

Today’s combined position on our country charts.

Mexico · Technology
#147 New on this chart
Pulse 9

Ranking history

History builds daily. We started tracking this show on the Technology chart recently, so the trend line appears after a second day of data.

About the show

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.

Latest episodes

  • 1. Machine Learning Lecture 1

    July 23, 2008 · 0 min

    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.

  • 2. Machine Learning Lecture 2

    July 23, 2008 · 0 min

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

  • 3. Machine Learning Lecture 3

    July 23, 2008 · 0 min

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

  • 4. Machine Learning Lecture 4

    July 23, 2008 · 0 min

    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.

  • 5. Machine Learning Lecture 5

    July 23, 2008 · 0 min

    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…

  • 6. Machine Learning Lecture 6

    July 23, 2008 · 0 min

    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.

  • 7. Machine Learning Lecture 7

    July 23, 2008 · 0 min

    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.

  • 8. Machine Learning Lecture 8

    July 23, 2008 · 0 min

    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.