An Overview of Artificial Intelligence and Its Application





Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

Artificial Intelligence: A Modern Approach (AIMA), 3rd Edition, 2009, Stuart Russell, Peter Norvig

  • Readership: advanced undergraduates and graduates, professionals
  • The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
  • Topics: Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty and Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics.
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Paradigms of Artificial Intelligence Programming: Case Studies in Common LISP (PAIP), 1992, Peter Norvig

  • Readership: advanced undergraduates, professionals
  • This book teaches how to build and debug robust practical AI systems, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.
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Knowledge Management

Knowledge management (KM) is the process of creating, sharing, using and managing the knowledge and information of an organisation.

Knowledge Representation, Reasoning and Declarative Problem Solving, 1st Edition, 2003 Chitta Baral

  • Readership: graduates, researchers, software pactitioners, knowledge engineers
  • Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog, for both knowledge representation and reasoning, and declarative problem solving.
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Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.

See Probability and Statistics for Computer Science for statistics for machine learning,

Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (IML), Feb 21 2019, Christoph Molnar

  • Readership: advanced undergraduates
  • This book focuses on interpretable machine learning models for tabular data (also called relational or structured data) and topics include computer vision and natural language processing tasks.
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Pattern Recognition and Machine Learning (PRML), 2011, Christopher M. Bishop

  • Readership: advanced undergraduates
  • Prerequisites: multivariate calculus, basic linear algebra
  • This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. , and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
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Machine Learning, A Probabilistic Perspective (MLAPP), 1st Edition, 2012, Kevin P. Murphy

  • Readership: researchers
  • Prerequisites:: basic multivariate calculus, probability, linear algebra, MATLAB programming
  • This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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Optimal Learning, 2012, Warren B. Powell, Ilya O. Ryzhov adaptive learning, information, randing and selection, knowledge gradient, bandit problems, linear belief models, subset selection problems, optimal bidding, stopping problems

  • Readership: undergraduates, profesionals
  • Prerequisites: an elementary background in probability and statistics
  • Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. This book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business.
  • This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems.
  • Structures: Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises.
    • Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems
    • Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems
    • Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements
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The Elementes of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition, 2016, Trevor Hastie, Robert Tibshirani, Jerome Friedman

  • Readership: advanced undergraduates, statisticians
  • This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.
  • Topics: supervised learning, unsupervised learning, neural networks, support vector machines, classification trees, boosting

Reinforcement Learning: An Introduction (RLAI), Nov 5, 2017, Richard S. Sutton, Andrew G. Barto

  • Readership: advanced undergraduates
  • Prerequisites: probability
  • This book provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In this book, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications.
  • Structure:
    • Part I defines the reinforcement learning problem in terms of Markov decision processes.
    • Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning.
    • Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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Highlighted Topics and Papers


Branches of Artificial Intelligence

By Approach:

By Application: