The eight sections are (1) Artificial Intelligence (introductory material); (2) Problem-Solving (search and game playing); (3) Knowledge and Reasoning (propositional and predicate logic, inference techniques, knowledge representation); (4) Acting Logically (planning); (5) Uncertain Knowledge and Reasoning (probabilistic reasoning, Bayesian nets, decision-theoretic techniques); (6) Learning (inductive learning, neural nets, reinforcement learning); (7) Communicating, Perceiving, and Acting (natural language processing, computer vision, robotics); and (8) Conclusions (philosophical foundations and summary). What makes this textbook so good? First, it is remarkably comprehensive. In the preface, the authors suggest several alternative paths through the book that could serve as the basis of a one-semester course. At the University of Pittsburgh, my colleagues and I cover roughly the first half of the book (Sections 1-4) in the firstsemester introductory graduate AI course, covering most of Sections 5 through 8 in a second-semester course.
"In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of'pattern recognition' or'machine learning'. "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning.
To ensure that readers fully understand the topic and its applications, the authors provide motivating examples throughout. AI in Practice boxes appear in each chapter, demonstrating real-world uses of artificial intelligence by NASA, General Motors Corporation, Microsoft Corporation, and other companies. LISP Implementation appendices are found at the end of most chapters, providing fully-documented implementations of important algorithms. These are carefully coordinated with the discussions in the chapters making it easy for students to complete computational experiments. Plus, the text features summaries, exercises, and background sections describing related work at the end of each chapter.
Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. In addition, recent topics, such as multi-armed bandits, learning to rank, group systems, multi-criteria systems, and active learning systems, are discussed together with applications. For subscribing institutions click from a computer directly connected to your institution network to download the book for free. To be eligible, your institution must subscribe to "e-book package English (Computer Science)" or "e-book package English (full collection)".
I would like to add on to the post. Image processing is a field that has existed on its own longer than machine learning (ie, it predates machine learning decades before), its been taught mainly as a branch of engineering (electrical & electronics) & to some lesser degree also taught in computer science & physics' courses. Its only in the last decade or so, that image processing includes machine learning topics' for image recognition & understanding. The latest edition (3rd) has an added chapter on "Object Recognition" which wasn't available in the 1st & 2nd edition. The last time I passed through my local university bookstore (about a year ago), this textbook is stocked because its still currently a prescribed textbook for final year Electrical engineering courses.