21–30 of 114 posts
"Introduction to Statistical Learning" - https://www.statlearning.com/ (there's also "Elements of Statistical Learning" which is a more advanced version) AI: A Modern Approach - https://aima.cs.berkeley.edu/
But is also available online as a preprint here: https://mlstory.org/
Haugeland is GOFAI/cognitive science, not directly relevant to modern machine learning variety of models unless you are doing reinforcement learning or trees stuff (hey poker/chess/Go bots are pretty cool!). Russel and Norvig are the typical introductory textbooks for those. Marks and Haykins are all severely out of date (they have solid content, but they don't have the same scale of modern deep learning which has ma…
Understanding Machine Learning: From Theory To Algorithms – Shai Shalev-Shwartz
In the opening chapter Jaynes describes a hypothetical system he calls “The Robot”. He then lays out the mathematics of the “The Robot’s” thinking in detail: essentially Bayesian probability theory. This is the best summary of an ideal ML/AI system I’ve come across. It’s also very philosophically enlightening.
I personally consider Linear algebra to be foundational in AI/ML. Intro to Linear algebra, Gilbert Strang. And his free course on MIT OCW is fantastic too. While having strong mathematical foundation is useful, I think developing intuition is even more important. For this, I recommend Andrew Ng's coursera courses first before you dive too deep.
I never took beyond Precalculus in school, thanks for the tip!
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 course is outstanding and available on EdX. The tradeoff is that LfD doesn't cover a lot of breath in terms of looking at specific a…
It’s probably a bit off the beaten path, but I can highly recommend Probability Theory, The Logic of Science, by E. T. Jaynes. In the opening chapter Jaynes describes a hypothetical system he calls “The Robot”. He then lays out the mathematics of the “The Robot’s” thinking in detail: essentially Bayesian probability theory. This is the best summary of an ideal ML/AI system I’ve come across. It’s also very philosophic…