I'm not interested in hands-on guides (eg. how to train a DNN classifier in TensorFlow) or LLM-centric resources.

So far, I've put together the following curriculum:

1 Artificial Intelligence: A Modern Approach (https://aima.cs.berkeley.edu/) - Great for learning the breadth of foundational concepts, eg. local search algorithms, building up to modern AI.

2 Probabilistic Machine Learning: An Introduction (https://probml.github.io/pml-book/book1.html) - Going more in-depth into ML.

3 Dive into Deep Learning (https://d2l.ai/) - Going deep into DL, including contemporary ideas like Transformers and Diffusion models.

4. Neural networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) could also be a great resource but the content probably overlaps significantly with 3.

Would anybody add/update/remove anything? (Don't have to limit recommendations to textbooks. Also open to courses, papers, etc.)

Sorry for the semi-redundant post.