Neural networks for pattern recognition

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… work in somewhat different domains and so have a different list of books. I hope someone finds this useful. For those who want a summary, Proof Technique (a)Velleman's "How to Prove It" (b)Gries and Schneider's "A Logical Approach to Discrete Math" Math (c) Calculus (best "lite" book - Calculus by Strang (free download), best "heavy" books - (d) Calculus by Spivak, (e) Principles of Mathematical Analysis a.k.a "Baby Rudin") (f) Discrete Math (ALADM above + (g) a good book on Algorithms, Cormen will do - though working through it comprehensively is ... hard! (h) Linear Algebra (First work through Strang's book, then (i) Axler's) (j) Probability (see Bradford's very comprehensive recommendations) and (k) Statistics (I would reccomend Devore and Peck for the total beginner but it is a damn expensive book. So hit a library or get a bootlegge…

my recommendations (for neural networks) would be as follows: - parallel distributed processing (PDP group vol 1 & 2) - simon haykin (Neural Networks and Learning Machines) - christopher bishop (Neural Networks for Pattern Recognition)

I agree about #2. I have Russell and Norvig and agree that it is an excellent book, but I am not sure how much overlap there truly is here. I also do not have PRML, but Neural Networks for Pattern Recognition by Bishop is excellent (and includes many non-NN related items). ESL is excellent and, to me, the best modern text in machine learning. It covers many of the topics in PCI both at a reasonable level and in much more depth and provides MANY references (100s?) to dig deeper.