Learning From Data
by Yaser S. Abu-Mostafa · first published 2012
76% recommend24% neutral
Why HN recommends it
Short, cohesive theory-first introduction to machine learning built around statistical learning theory and VC dimension, with free Caltech video lectures; suits self-study and people wanting rigor without PRML's sprawl.
Summarised by Claude from recommending comments
Affiliate links. As an Amazon Associate I earn from qualifying purchases.
Mentions per year
What Hacker News says
Related books
2007–2023Pattern classification
Richard O. Duda, Peter E. Hart, David G. Stork
Recommended by 8 · mentioned by 98 recommend · 9 mention8 recommend · 98 recommend
2014–2023Understanding Machine Learning
Shai Shalev-Shwartz, Shai Ben-David
Recommended by 12 · mentioned by 1612 recommend · 16 mention12 recommend · 1612 recommend
2014–2025Machine learning
Peter A. Flach
Recommended by 1 · mentioned by 41 recommend · 4 mention1 recommend · 41 recommend
2014–2025Foundations of machine learning
Mehryar Mohri
Recommended by 4 · mentioned by 84 recommend · 8 mention4 recommend · 84 recommend
2007–2026Pattern Recognition and Machine Learning
Christopher M. Bishop
Recommended by 80 · mentioned by 10480 recommend · 104 mention80 recommend · 10480 recommend
2015–2021Writing better lyrics
Pat Pattison
Recommended by 2 · mentioned by 22 recommend · 2 mention2 recommend · 22 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
2014–2026Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Recommended by 85 · mentioned by 10885 recommend · 108 mention85 recommend · 10885 recommend
To tag onto this, I found "Learning from Data" [0] by Abu Mastafa to be a great intro to the field. It's not heavy on the math, but it doesn't gloss over it either [0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...
on “A New Book: Building Machine Learning Systems with Python” ↗…from Stanford. http://infolab.stanford.edu/~ullman/mmds.html Bayesian Reasoning and Machine Learning, by Barber, free available online. http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=... Learning from data, by Abu-Mostafa. It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html Pattern Recognition and Machine Learning, by Bischop http://research.microsoft.com/en-us/um/people/cmbishop/prml/…
on “A Tour of Machine Learning Algorithms” ↗Learning from Data by Caltech Professor Yaser Abu-Mostafa : https://work.caltech.edu/telecourse.html. There was an edX version last year MMDS: Mining Massive Datasets by Stanford professors Jure Leskovec, Anand Rajaraman,Jeff…
on “Ask HN: What's your favorite online course?” ↗…experience in the problem domain, even better. Data is driving everything so developing a data analysis/machine learning skillset will put you into any industry you want. Professor Yaser Abu-Mostafa's "Learning From Data" is a gem of a course that helps you see the physics underpinning the learning (metaphorically of course -- ML is mostly vectors, matrices, linear algebra and such). The course videos are online for…
on “Ask HN: Going back to programming” ↗Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works. See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw…
on “CMU's Introduction to Machine Learning Course” ↗It depends on your math background, but I've found Yaser S. Abu-Mostafa's Learning From Data the best, although Christopher M. Bishop's Pattern Recognition and Machine Learning is the gold standard. ;-)
on “A Tour of Machine Learning Algorithms” ↗…Coursera: https://www.coursera.org/course/ml It's pretty light on math, while at the same time giving you experience in implementing and understanding these techniques. From there, I might recommend Learning from Data and the associated video lectures: https://work.caltech.edu/telecourse.html It is a bit of a jump, but it is a great course in presenting the field of machine learning and explaining the mathematical…
on “Machine Learning Course by Tom Mitchell” ↗Abu-Mostafa's Learning From Data[0] was more more rigorous, in my experience. It's a full-fledged Caltech course, sometimes taught on EdX concurrently with on-campus sessions. The Ng class was a good introduction, but it was mostly…
on “Andrew Ng is raising a $150M AI Fund” ↗…though. As somebody who didn't study CS in college, the books that most changed my career were: * Algorithms, by Robert Sedgwick (probably not the best algorithms book, but lots of hands-on stuff) * Learning From Data, by Yaser S. Abu-Mostafa et. al. This one has video lectures to go along with it. * C Programming: A Modern Approach. (Definitely not the best, but also lots of hands on stuff) Another thing I've found…
on “Books that changed my career as a software engineer” ↗This is off the beaten path, but consider Abu-Mostafa et al.'s "Learning from Data". https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/... I adore PRML, but the scope and depth is overwhelming. LfD encapsulates a number of really core principles in a simple text. The companion…
on “Ask HN: What are the foundational texts for learning about AI/ML/NN?” ↗The best introductory book and also the most cohesive one is "Learning from Data" from Yaser Abu-Mostafa, accompanied by great video lectures: http://amlbook.com/ It differs from other books in that all the material is treated from the unified perspective of statistical learning…
on “Probabilistic Programming & Bayesian Methods for Hackers” ↗For a glimpse into machine learning, check out Professor Yaser Abu-Mostafa's "Learning From Data" course from Caltech. The videos are online for free (http://work.caltech.edu/telecourse.html, https://www.edx.org/course/caltechx/cs1156x/learning-data/11...), and its corresponding book is on Amazon…
on “What are the math heavy CS areas with high demand?” ↗You can do a MOOC with very little watering down. Yaser Abu-Mostafa's "Learning From Data" from Caltech in an example. Here's an article written by Abu-Mostafa about this [1]. [1] https://www.class-central.com/report/caltech-mooc-flipped-cl...
on “Fundamentals of Neuroscience from Harvard EdX” ↗I really enjoyed the books "Learning From Data"[1], and "Programming Collective Intelligence"[2]. Both are accessible to beginners. Learning From Data gives a more theoretical introduction to machine learning. One of the central ideas from the book…
on “Implementing a Neural Network from Scratch – An Introduction” ↗On that note, one of the best books I've read for teaching the concepts is Learning From Data by Yaser Abu-Mostafa et al. I highly recommend it as a way to refresh or introduce the fundamentals: http://amlbook.com/
on “Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning” ↗