Models of Computation by John E. Savage
This book, written for graduates, covers general subjects on computational models, logic circuits, and memory machines, with advanced subjects being parallel computation, circuit complexity, and space-time trade-offs; therefore, it's a very thorough course on computational models and their complexities.
Author(s): John E. Savage, Brown University
698 Pages 
Introduction to the Theory of Computation Lecture Notes
These are lecture notes from the University of Toronto, giving a very brief introduction to some of the basic ideas in the theory of computation. We start with some basic topics: induction and recursion; the correctness of programs, that must be understood if more advanced computational theories are to be enlightened. Then we go on to develop the topics of regular languages and finite automata, giving the basic models and techniques used in analysing and recognising regular languages. The coverage is designed to provide students with a reasonably solid grounding in the basic ideas of the theory of computation and to render a clear and thorough exposition of the fundamental concepts underlying more advanced topics.
Author(s): University of Toronto
75 Pages
Lecture Notes of Introduction to the Theory of Computation
Authored by Margaret Fleck and Sariel Har Peled, this is a wide set of lecture notes on the theory of computation. These start with the very basic objects such as strings and deterministic finite automata (DFAs) before moving up to regular expressions and nondeterministic automata. This course covers formal language theory, including some advanced topics such as Turing machines, decidability, and several language-related problems. It is intended that these notes afford a comprehensively broad yet deep exploration of the formal languages, automata, and computability material with an excellent bibliography that creates interest among students and researchers.
Author(s): Margaret Fleck and Sariel Har Peled
345 Pages
Theory of Computation by Kyle Burke
This book surveys some of the most relevant theoretical concepts with computational models. The limits of computation, undecidability of the Halting Problem, several automata models, including both deterministic and nondeterministic finite-state automata, pushdown automata, and Turing machines, are introduced. The ending is dedicated to computational complexity, with NP-Completeness, approximation algorithms, and hardness of approximation.
Author(s): Kyle Burke
114 Pages
Models of Computation by John E. Savage
This book, written for graduates, covers general subjects on computational models, logic circuits, and memory machines, with advanced subjects being parallel computation, circuit complexity, and space-time trade-offs; therefore, it's a very thorough course on computational models and their complexities.
Author(s): John E. Savage, Brown University
698 Pages
Introduction to Theory of Computation Lecture Notes
These lecture notes give an introduction to the more fundamental parts of the theory of computation and begin by presenting finite automata: starting with deterministic and nondeterministic finite automata, their equivalence, and practical implications of these concepts. The lecture notes include sections on regular expressions and their relationship to finite automata, non-regular languages, and the Pumping Lemma to prove non-regularity. Myhill-Nerode Theorem: For understanding recognition of languages. The notes go further to present context-free languages, including their ambiguity and properties of closure. The pumping lemma for context-free languages is also discussed, while decidable and recognizable languages are informed by a deep underpinning in computational theory.
Author(s): Mahesh Viswanathan
NA Pages
Advanced Theory in Computation
This is an advanced set of notes on the analysis of algorithms and their complexity. Of interest in these notes are the topics on string matching algorithms, such as Knuth-Morris-Pratt and Boyer-Moore. Suffix trees and dictionary techniques are also part of the discussion here. Among the methods to be shown in a way of analyzing algorithm efficiency are amortized analysis and randomized algorithms. It also treats the pairing technique, Ziv-Lempel coding; further topics on statistical adversaries, portfolio selection, and reservation-price policies that are objects of other techniques discussed herein.
Author(s): Seoul National University
NA Pages
