Foundations of Data Science
by Avrim Blum, John Hopcroft, Ravindran Kannan · first published 2020
29% recommend54% neutral17% critical
Why HN recommends it
Theory-focused treatment of the algorithms and mathematical properties behind data science, including high-dimensional geometry, sampling and probability; suited to readers wanting rigorous foundations for ML.
Common criticism: Some find it more algorithms and theory than the title suggests, with little coding or applications.
Summarised by Claude from recommending comments
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…Machine Learning If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website. - Machine…
on “Ask HN: Full-on machine learning for 2020, what are the best resources?” ↗…different author names attached. The linked book doesn't suffer from any of these problems. [0] Mathematics For Machine Learning by Deisentoth, Faisal, Ong https://mml-book.github.io/book/mml-book.pdf [1] Foundations Of Data Science By Blum, Hopcroft, Kannan http://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March%... 2] Pure Mathematics for Beginners: A Rigorous Introduction to Logic, Set Theory, Abstract Algebra, Number Theory,…
on “Math Basics for Computer Science and Machine Learning [pdf]” ↗…gaussians or other distributions which are separated from each other or b) that projections of one of these distributions can't have a different distribution than a projection from the other. Please go read Foundations of Data Science, chapters 1 and 2 before expounding on this topic further: http://research.microsoft.com/en-US/people/kannan/book-no-so... Can you clearly state what, precisely, you think this article proves about either…
on “When the U.S. air force discovered the flaw of averages” ↗…are extremely theoretical, anyone interviewing for these kind of roles should spend at least a week or two just looking through this to see what kind of algorithms and properties are studied in theory. Foundations of Data Science (Blum, Hopcroft, Kannan) https://www.cs.cornell.edu/jeh/book2016June9.pdf
on “A Student's Guide to Preparing for Data Science Interviews” ↗…didn't expect to see Hopcroft/Kannan in that list. I used it in a course taught by Kannan and back then it was called "Computer Science Theory for the Information Age". Apparently the book is now called "Foundations of Data Science". I always thought the old naming was terrible but compared to the new title it actually describes the content a lot better since the book is mainly about CS theory and mathematical foundations for them.…
on “Machine Learning Crash Course: Part 2” ↗…such books. For example, elementary stuff[0] and more advanced stuff[1]. [0] Math For Machine Learning by Deisenroth, Faisal, Ong: https://www.google.com/books/edition/Mathematics_for_Machine... [1] Foundations of Data Science by Blum, Hopcroft, Kannan: https://ttic.uchicago.edu/~avrim/book.pdf
on “Mathematics for Computer Science (2017) [pdf]” ↗I learned the easiest way to do d-dimension sampling in Foundations of Data Science: see https://news.ycombinator.com/item?id=34575637 or https://www.cs.cornell.edu/jeh/book.pdf?file=book.pdf I don't think it's a good idea to introduce over 20 different methods before talking about…
on “How to generate uniformly random points on n-spheres and in n-balls” ↗For those who know, How does this book differ from Foundations of Data Science by Blum/Hopcroft/Kannan ? http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pd... ?
on “Computer Age Statistical Inference: Algorithms, Evidence and Data Science” ↗Related but different: Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan: https://www.cs.cornell.edu/jeh/book2016June9.pdf
on “Lecture Notes on Randomized Linear Algebra (2013)” ↗…probability. Since I prefer books written in definition - theorem - proof format anyway, I figured I might as well get analysis out the way :) [1]http://www.cs.cornell.edu/jeh/book%20June%2014,%202017pdf.pd... (Foundations of Data Science by Bloom/Hopcroft/Kannan)
on “Predict the future with Machine Learning” ↗there was discussion also on threads about the book "Foundations of Data Science" by Hopcroft /Kannan/Blum but i can't find those threads
on “An Adventure in the Nth Dimension (2011) [pdf]” ↗Foundations of Data Science Avrim Blum, John Hopcroft, and Ravindran Kannan Copyright 2015
on “Ask HN: 'Crash Courses' for Mathematics Related to DL, ML and Data Analysis” ↗…a few undergrad/high school level books on the subject, but I am not sure how useful that is to a freshman. [0] Book Of Proof by Richard Hammack https://www.people.vcu.edu/~rhammack/BookOfProof/ [1]Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March...
on “Ask HN: I'm an incoming freshman to college for a CS major, what should I know?” ↗…then, so chances are it is still good. If you want something more theoretical there is a book by Hopcroft et al. that was released in draft form a number of years ago. It appears to be out for real now: Foundations of Data Science, by Avrim Blum, John Hopcroft, and Ravindran Kannan. Blurb and video lectures: https://www.microsoft.com/en-us/research/publication/foundat... I just found these so haven't looked at them yet. The…
on “Ask HN: In 2022, what is the proper way to get into machine/deep learning?” ↗…bursting to get out. E.g. I'd like to be clueful about AI, which to me I think means studying stuff like high dimensional analysis, rather than "How to use ChatGPT". Blum, Hopcroft, and Kannan's book "Foundations of Data Science" looks really good for that: https://www.cs.cornell.edu/jeh/book%20no%20so;utions%20March... I got a page or two into it and had to put it aside, but someday. The book is officially published now (on…
on “Ask HN: What would you do if you didn't have to work?” ↗