1–10 of 114 posts
Pattern Recognition and Machine Learning - Bishop
Deep Learning - Goodfellow, Bengio, Courville
Neural Smithing - Reed, Marks
Neural Networks - Haykin
Artificial Intelligence - Haugeland
(there's also "Elements of Statistical Learning" which is a more advanced version)
AI: A Modern Approach - https://aima.cs.berkeley.edu/
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.
The first chapter walks through a neural network that recognizes handwritten digits implemented in a little over 70 lines of Python and leaves you with a very satisfying basic understanding of how neural networks operate and how they are trained.
You are approaching this like an established natural sciences field where old classics = good. This is not true for ML. ML is developing and evolving quickly.
I suggest taking a look at Kevin Murphy's series for the foundational knowledge. Sutton and Barto for reinforcement learning. Mackay's learning algorithms and information theory book is also excellent.
Kochenderfer's ML series is also excellent if you like control theory and cybernetics
https://algorithmsbook.com/ https://mitpress.mit.edu/9780262039420/algorithms-for-optimi... https://mitpress.mit.edu/9780262029254/decision-making-under...
For applied deep learning texts beyond the basics, I recommend picking up some books/review papers on LLMs, Transformers, GANs. For classic NLP, Jurafsky is the go-to.
Seminal deep learning papers: https://github.com/anubhavshrimal/Machine-Learning-Research-...
Data engineering/science: https://github.com/eugeneyan/applied-ml
For speculation: https://en.m.wikipedia.org/wiki/Possible_Minds
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.
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 Marks and Haykins are all severely out of date (they have solid content, but they don't have the same scale of modern deep learning which has ma…
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…