Foundations of machine learning
by Mehryar Mohri · first published 2012
50% recommend50% neutral
Affiliate links. As an Amazon Associate I earn from qualifying purchases.
Mentions per year
What Hacker News says
Related books
2014–2023Understanding Machine Learning
Shai Shalev-Shwartz, Shai Ben-David
Recommended by 12 · mentioned by 1612 recommend · 16 mention12 recommend · 1612 recommend
2011–2025Learning From Data
Yaser S. Abu-Mostafa
Recommended by 31 · mentioned by 4131 recommend · 41 mention31 recommend · 4131 recommend
2007–2026The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Recommended by 144 · mentioned by 227144 recommend · 227 mention144 recommend · 227144 recommend
2014–2026Foundations of Data Science
Avrim Blum, John Hopcroft, Ravindran Kannan
Recommended by 7 · mentioned by 247 recommend · 24 mention7 recommend · 247 recommend
2007–2026Pattern Recognition and Machine Learning
Christopher M. Bishop
Recommended by 80 · mentioned by 10480 recommend · 104 mention80 recommend · 10480 recommend
2010–2025Probabilistic Graphical Models
Daphne Koller, Nir Friedman
Recommended by 16 · mentioned by 3716 recommend · 37 mention16 recommend · 3716 recommend
2015–2026Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
Recommended by 44 · mentioned by 8244 recommend · 82 mention44 recommend · 8244 recommend
2014–2026Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Recommended by 85 · mentioned by 10885 recommend · 108 mention85 recommend · 10885 recommend
…those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles…
on “Level-Up Your Machine Learning” ↗…(http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693...) Statistical/Theoretical: Shai Shalev-Schwartz & Shai Ben-David's Understanding Machine Learning (http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...) Mohri's Foundations of Machine Learning (https://cs.nyu.edu/~mohri/mlbook/) The two above courses could share SSS's Online Learning text (https://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf). To be fair, the stochastic variants of most optimization…
on “Confession as an AI researcher; seeking advice” ↗Also consider Foundations of Machine Learning (2012) by Meryar Mohri. It is a very good book with a statistical learning approach to ML. If you're interested by statistical guarantees to ML algorithms, eg. why the SVM algorithm works and its sample…
on “Machine Learning Crash Course: Part 2” ↗I recommend Shai Shalev-Schwartz and Shai Ben-David's Understanding Machine Learning: From Theory to Algorithms [0]. I've also used and found Mohri's Foundations of Machine Learning quite insightful [1]. Usually, between the two books, at least one's proof is easy to follow. Get both unless you're only getting one, in which case, get Shai Shalev-Schwartz's. [0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...…
on “Book: Mathematics for Machine Learning” ↗* Foundations of Machine Learning * All of Statistics * Doing Bayesian Data Analysis Also the ML specialization on Coursera
on “Free “Deep Learning” Textbook by Goodfellow and Bengio Now Finished” ↗…somebody else's library, but if you're developing your own algorithms or you want to engage seriously with the research literature, you have to know what's in here. The main other books I'd consider are Foundations of Machine Learning (Rostamizadeh, Talwalkar & Mohri) and Machine Learning: A Probabilistic Perspective (Murphy). There are a few others, but I think you'd need a good reason not to pick one of those three.
on “Understanding Machine Learning: From Theory to Algorithms” ↗This looks interesting. Does anyone knows how it compares to other learning theory books like foundations of machine learning [1] in terms of depth and approachability? [1] https://cs.nyu.edu/~mohri/mlbook/
on “Learning Theory from First Principles [pdf]” ↗I would like to read mathematical foundations of machine learning written for those who are bad at calculus but good at discrete mathematics and algorithms. For example, I'm learning algorithms, participate at contests, quite comfortable with combinatorics and discrete…
on “Predict the future with Machine Learning” ↗Along the same lines, here is a course on the "Mathematical Foundations of Machine Learning" [1] (from GT, but other institutions also having offerings) and is a section in the paper in this post (so deeper dive possible): [1.] https://jrom.ece.gatech.edu/mfml-f20-notes/
on “The Mathematics of Artificial Intelligence (2022)” ↗