[fgimenez]

- Norvig's AI: Doesn't have much deep learning, but you get through it and understand the expansiveness of the field.

- Algorithms - Papadimitrou and Vazirani: I had a professor who described this as a poetry book about algorithms. Alternative is Sipser

- An Introduction to Statistical Learning: This is like a diet form of Elements of Statistical Learning which is much more approachable and pragmatic.

- Janeway's Immunobiology - De facto standard of immunology. Great.

- SICP: duh

- Principles of Data Integration: This is more because the subject matter is so important and nobody really has studied fundamentals. Did you know general data integration is AI-complete? If 99% of work in AI was spent on data integration, the field would move so much faster.

[konz]

Assuming you are referring to Russell & Norvig's Artificial Intelligence: A Modern Approach, a 4th Edition was just released, including chapters about deep learning. With contributions from amongst others: Ian Goodfellow about Deep Learning and Judea Pearl about Causal Networks (see http://aima.cs.berkeley.edu/ack.html)

http://aima.cs.berkeley.edu

https://www.pearson.com/us/higher-education/program/Russell-...