Machine learning
by Stephen Marsland · first published 2009
6mentions
5recommended by
6HN commenters
2009–2014first – last mentioned
83% recommend17% neutral
Affiliate links. As an Amazon Associate I earn from qualifying purchases.
Mentions per year
What Hacker News says
Related books
2007–2021Data mining
I. H. Witten, Ian H. Witten, Eibe Frank
Recommended by 9 · mentioned by 149 recommend · 14 mention9 recommend · 149 recommend
2007–2025Programming Collective Intelligence
Toby Segaran
Recommended by 86 · mentioned by 11986 recommend · 119 mention86 recommend · 11986 recommend
2008–2026Principles of Mathematical Analysis
Walter Rudin
Recommended by 52 · mentioned by 8852 recommend · 88 mention52 recommend · 8852 recommend
2008–2026Probability Theory
E. T. Jaynes
Recommended by 77 · mentioned by 13877 recommend · 138 mention77 recommend · 13877 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
"Machine Learning: An Algorithmic Perspective" by Stephen Marsland is very good if you want to see the ideas in code and if you're not mathematically ready for hastie or bishop. http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html
on “Ask HN: I want to learn statistics and data mining” ↗To get the spirit of many of these techniques with practical examples in python using numpy/scipy check out the book "Machine Learning: An Algorithmic Perspective" by Stephen Marsland. It doesn't have the mathematical depth or proofs found in these other books, but the code is decent and will get you started doing some basic data analysis.…
on “Learning About Statistical Learning” ↗I'd like to add that Stephen Marsland's 'Machine Learning: An Algorithmic Perspective' is a new book in the same practical/introductory class. The code is mostly numpy. http://www-ist.massey.ac.nz/smarsland/MLbook.html
on “O'Reilly discounts: machine learning, nltk, hadoop, sql, nosql, aws and appeng” ↗What I have in the room with me: 1. Modern Algebra by John R. Durbin 2. Introduction to the Theory of Computation by Michael Sipser 3. Machine Learning - An Algorithmic Perspective by Stephen Marsland (not quite on the same level as the above two, but still pretty solid) I think Tanenbaum's Operating System and Appel's Compilers books both go in this…
on “Kindle and Nook readers bash high e-book pricing with angry one-star reviews” ↗You can check out Machine Learning: An Algorithmic Perspective by Stephen Marsland[0] which takes a less math-driven approach to ML. The code is available online if you want to take a look at it (it's written in Python)[1]. [0]: http://www.crcpress.com/product/isbn/9781420067187…
on “Deep Learning for NLP without Magic (2013)” ↗…majors do for first 2 years, plus probability/stats: https://www.seas.harvard.edu/courses/cs281/#schedule http://www.zipfianacademy.com/blog/post/46864003608/a-practi... http://fastml.com/math-for-machine-learning/ ________ the general purpose programming languages mostly used are python, C++, java, and the statistical languages include R, matlab. there's lots of good threads at /r/machineLearning, including…
on “Ask HN: Can I get into machine learning research after getting my medical degree?” ↗