Hopcroft+Ullman is very proof oriented. Sometimes the proof is constructive (by giving an algorithm and proving its correctness). I liked it. There may be much better available for self-study.
Specialty algorithms: I briefly referenced Numerical Optimization and it seems better than Numerical Recipes in C. I didn’t read it cover to cover.
Algorithms on Strings, Trees, and Sequences (Gusfield) was definitely a good source for computational biology algorithms (I don’t do computation biology, but it explains fairly well things like suffix trees and their applications, and algorithms matching a set of patterns against substrings of running text).
Foundations of Natural Language Processing is solid. I don’t think there’s a better textbook (for the types of dumb, statistics/machine-learning based, analysis of human speech/text that are widely practiced). It’s better than “Natural Language Understanding” (Allen), which is more old-school-AI.
Hopcroft+Ullman is very proof oriented. Sometimes the proof is constructive (by giving an algorithm and proving its correctness). I liked it. There may be much better available for self-study.
Specialty algorithms: I briefly referenced Numerical Optimization and it seems better than Numerical Recipes in C. I didn’t read it cover to cover.
Algorithms on Strings, Trees, and Sequences (Gusfield) was definitely a good source for computational biology algorithms (I don’t do computation biology, but it explains fairly well things like suffix trees and their applications, and algorithms matching a set of patterns against substrings of running text).
Foundations of Natural Language Processing is solid. I don’t think there’s a better textbook (for the types of dumb, statistics/machine-learning based, analysis of human speech/text that are widely practiced). It’s better than “Natural Language Understanding” (Allen), which is more old-school-AI.