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CS109 Probability for Computer Scientists
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#433 overall

CS109 Probability for Computer Scientists

Stanford University

1 recommender · 1 thread · recommended in 1 year, 2025 · score 1.0

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CS109 is Stanford's introductory probability course for computer scientists, taught by Chris Piech, covering probability, statistics, and their use in computing and machine learning. Commenters point to its free lecture videos and a downloadable course reader.

Commenters call it a theory-heavy treatment of probability underlying machine learning, which they rate better than other courses they have seen, and they note the free course reader.

Mathematicsintermediatefreevideo lectures#probability#statistics#machine learning#piech#stanford

Recommendations by year

20062026

What commenters said (3)

  1. Strongly recommendsrottc0dd2025in “Understanding Machine Learning: From Theory to Algorithms”

    From my other comment elsewhere. These resources helped me understand the topics better.

    If anyone wants to understand fundamentals of machine learning, one of the superb resources I have found is, Stanford's "Probability for computer scientists"[1].

    It goes into theoretical underpinnings of probability theory and ML, IMO better than any other course I have seen. But, this is a primarily a probability course that discusses the fundamentals of machine learning. (Yeah, Andrew Ng is legendary, but his course demands some mathematical familiarity with linear algebra topics)

    There is a course reader for CS109 [2]. You can download pdf version of this. Caltech's learning from data was really good too, if someone is looking for theoretical understanding of ML topics [3].

    There is also book for excellent caltech course[4].

    Also, neural networks zero to hero is for understanding how neural networks are built from ground up [5].

    [1] https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4...

    [2] https://chrispiech.github.io/probabilityForComputerScientist...

    [3] https://work.caltech.edu/telecourse

    [4] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

    [5] https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxb...

  2. Mentionsrottc0dd2025in “Deep Learning Is Not So Mysterious or Different”

    From, a comment I posted elsewhere for written versions.

    There is a course reader for CS109 [1]. You can download pdf version of this.

    There is also book[2] for excellent caltech course[3].

    [1] https://chrispiech.github.io/probabilityForComputerScientist...

    [2] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

    [3] https://work.caltech.edu/telecourse

  3. Mentionsrottc0dd2025in “Deep Learning Is Not So Mysterious or Different”

    There is a course reader for CS109 [1]. You can download pdf version of this.

    There is also book[2] for excellent caltech course[3].

    [1] https://chrispiech.github.io/probabilityForComputerScientist...

    [2] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

    [3] https://work.caltech.edu/telecourse

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