20 episodes

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.

Machine Learning Andrew Ng

    • Technology
    • 3.9 • 158 Ratings

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.

    • video
    1. Machine Learning Lecture 1

    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
    • video
    2. Machine Learning Lecture 2

    2. Machine Learning Lecture 2

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

    • 4 sec
    • video
    3. Machine Learning Lecture 3

    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
    • video
    4. Machine Learning Lecture 4

    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
    • video
    5. Machine Learning Lecture 5

    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
    • video
    6. Machine Learning Lecture 6

    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

Customer Reviews

3.9 out of 5
158 Ratings

158 Ratings

Felixrpl ,

Inspiring

Awesome and very inspiring lectures to achieve things with the machine learning.

MarryC1 ,

awesome course

I took this course and it is the way to get started with fundementals of ML.

Улугбек ,

Thank you so much

Really super course. Thank you prof. Ng and Stanfor U.

Top Podcasts In Technology

Lex Fridman Podcast
Lex Fridman
All-In with Chamath, Jason, Sacks & Friedberg
All-In Podcast, LLC
No Priors: Artificial Intelligence | Machine Learning | Technology | Startups
Conviction | Pod People
Hard Fork
The New York Times
Acquired
Ben Gilbert and David Rosenthal
TED Radio Hour
NPR

You Might Also Like

Practical AI: Machine Learning, Data Science
Changelog Media
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Super Data Science: ML & AI Podcast with Jon Krohn
Jon Krohn
The AI Podcast
NVIDIA
Machine Learning Street Talk (MLST)
Machine Learning Street Talk (MLST)
Machine Learning Guide
Dept

More by Stanford

The Future of Everything
Stanford Engineering
Human Behavioral Biology
Robert Sapolsky
Stanford Legal
Stanford Law School
Modern Physics: General Theory of Relativity (Fall 2012)
Stanford Continuing Studies
Modern Physics: Quantum Mechanics (Winter 2012)
Leonard Susskind
Historical Jesus
Stanford Continuing Studies Program