undergraduate machine learning 4: Introduction to probability, linear algebra and pagerank

Introduction to conditional probability, matrix representation of discrete probabilities, Bayes rule and examples, such as pagerank for web search and medical diagnosis.
The slides are available here:
This machine learning course was taught in 2012 at UBC by Nando de Freitas

12 comments

  1. forun2013 says:

    Thank you very much Professor De Freitas – It is extremely kind of you to
    give us access to your video. Unfortunately, I am little bit old to tacke a
    Phd now.

  2. 225Noah says:

    Thanks for the video. Is there any resource that describes the connection
    between the second eigen value and the rate of convergence without all the
    jargons related to special type of vectors and matrices.

  3. Denis Wilson Souza Rosa says:

    I have been watching all your lectures and I am really amazed with the high
    quality of them. Thanks for publishing it.

  4. Marija Minic says:

    Should you be looking to have higher rank on internet search engine, i
    recommend using “googleranktool weebly com”. Just google it on search
    engine.

  5. ajay singh says:

    “i’ve built search engines, i’ve sold search engines for shit loads of
    money” best line from a professor during a lecture!!!

  6. Sarit Kiran says:

    Why is G not having any suffix like G1, G2, G3, etc? What I see is that G1
    is P(X2|X1), and G2 should be P(X3|X2)… Thus G seems to be changing
    according to me. How can we then write PI1*G^(K-1) = PIK?

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