Lecture 11 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:

CS 229 Course Website:

Stanford University:

Stanford University Channel on YouTube:

12 comments

  1. kent norton says:

    dont they have computers or technological tools that the prof can use. Was
    this video made in 1955 in the Muncie Central audio visual room in the
    basement. There are many finite examples of his style that bore the hell
    out of me. Dante’s inferno must have a ring near the devil for this guy

  2. Weiwei Cheng says:

    I like his style. Using slides is boring. Using the board makes you think
    along with the prof.

  3. handdancin says:

    I will actually be sad when i get to the end of these lectures… they are
    a joy to watch. thanks Professor Ng :)

  4. Saksham Gautam says:

    Thank you for sharing such valuable tips and advice for applying machine
    learning algorithms.

  5. skyaak says:

    Great great video.. I liked the way he linked to real life problems… Hats
    off Prof. Andrew…. I wish I had guide like you for my PhD. Watching you
    and reading your materials, you are already some on some part of my PhD. 

  6. Jay P says:

    28:00 Diagnostics. Techniques for dealing with underfitting, overfitting,
    incorrect optimization functions, and poorly chosen algorithms.

Comments are closed.