The Online Books Page

Machine theory

Here are entered works on the abstract mathematical models of devices which operate within a consistent logical system in accordance with a given algorithm; such mathematical theory can be used as a basis for design but is not concerned with hardware.

See also what's at Wikipedia, your library, or elsewhere.

• Algorithms
• Logic, Symbolic and mathematical
• Recursive functions

• Robotics

• Machine theory -- Congresses
• Machine theory -- Periodicals
• Artificial intelligence
• Coding theory
• Computational complexity
• Computers
• Control theory
• Formal languages
• L systems
• Machine learning
• Machine-to-machine communications
• Sequential machine theory
• Switching theory

• Abstract automata
• Abstract machines
• Automata
• Mathematical machine theory
• Automata theory

• Algorithmic Information Theory (based on the third printing, 1990), by Gregory J. Chaitin (PDF at Maine)
• Information-Theoretic Incompleteness, by Gregory J. Chaitin (Postscript at Maine)
• The Unknowable, by Gregory J. Chaitin (illustrated HTML at Maine)

• RAIRO: Theoretical Informatics and Applications (partial serial archives)

• AI for Everyone? Critical Perspectives (London: University of Westminster Press, c2021), ed. by Pieter Verdegem (multiple formats with commentary at University of Westminster Press)
• Artificial Intelligence and Games (2018), by Georgios N. Yannakakis and Julian Togelius (PDF with commentary at gameaibook.org)
• Artificial Intelligence and Responsive Optimization (second edition, 2003), by Mohammad Khosnevisan, Sukanto Bhattacharya, and Florentin Smarandache (PDF at UNM)
• Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong A.I. (c2002), ed. by Jay W. Richards, contrib. by Ray Kurzweil, John R. Searle, Michael J. Denton, William A. Dembski, Thomas S. Ray, and George F. Gilder (at kurzweilai.net)
• Artificial Intelligence (third edition, reprinted with corrections; Reading, MA et al.: Addison-Wesley Pub. Co., 1993), by Patrick Henry Winston (PDF at MIT)
• The Age of Intelligent Machines (c1992), by Ray Kurzweil (at kurzweilai.net)
• The Boundaries of Humanity: Humans, Animals, Machines (Berkeley: University of California Press, 1991), ed. by James J. Sheehan and Morton Sosna (HTML at UC Press)
• Artificial Intelligence Through Prolog (1988), by Neil C. Rowe (HTML at nps.edu)
• How Mobile Robots can Self-Organise a Vocabulary (Berlin: Language Science Press, c2015), by Paul Vogt
  • PDF with commentary at langsci-press.org
  • PDF at OAPEN
• Applications of Robotics and Artificial Intelligence to Reduce Risk and Improve Effectiveness: A Study for the United States Army (1983), by National Research Council Commission on Engineering and Technical Systems (page images with commentary at NAP)
• When Things Start to Think (c1999), by Neil A. Gershenfeld (HTML with commentary at kurzweilai.net)
• Elements of Robotics (Cham: Springer Open, c2018), by M. Ben-Ari and Francesco Mondada (PDF and EPub with commentary at EPFL and Springer)
• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp (c1992), by Peter Norvig (multiple formats with commentary at Github)
• From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots (Edmonton: AU Press, c2010), by Michael Robert William Dawson, Brian Dupuis, and Michael Wilson (PDF with commentary at AU Press)
• The Computer Revolution in Philosophy: Philosophy, Science and Models of Mind (1978, with added notes and corrections), by Aaron Sloman (HTML and PDF with commentary in the UK)
• The Origin and Its Meaning, by Roger Ellman (PDF files at the-origin.org)

• Entropy and Information Theory (electronic edition, 2000), by Robert M. Gray (PDF at Stanford)
• Sequential Decoding (research monograph #10; Cambridge, MA: MIT Press, c1961), by John M. Wozencraft and Barney Reiffen (page images at HathiTrust)

• Mathematics and Computation: A Theory Revolutionizing Technology and Science (prepublication version, c2019), by Avi Wigderson (PDF with commentary at ias.edu)
• Think Complexity (second edition; Needham, MA: Green Tea Press, c2012), by Allen Downey (multiple formats at Green Tea Press)
• A New Kind of Science (Champaign, IL: Wolfram Media, c2002), by Stephen Wolfram (illustrated HTML at wolframscience.com)
• Algorithmic Information Theory (based on the third printing, 1990), by Gregory J. Chaitin (PDF at Maine)
• Algorithms and Complexity (first edition, 1994), by Herbert S. Wilf (PDF with commentary here at Penn)
• Cellular Automata and Complexity: Collected Papers, by Stephen Wolfram (PDF files at stephenwolfram.com)
• The Complexity of Boolean Functions (electronic edition), by Ingo Wegener (PDF with commentary at Trier)
• Information-Theoretic Incompleteness, by Gregory J. Chaitin (Postscript at Maine)
• The Unknowable, by Gregory J. Chaitin (illustrated HTML at Maine)
• Chaotic Logic: Language, Thought and Reality From the Perspective of Complex Systems Science (prepublication version), by Ben Goertzel (HTML at goertzel.org)

• How Computers Work: Processor and Main Memory (c2009), by Roger Young (HTML, PDF, and Word at fastchip.net)
• Tools For Thought: The People and Ideas of the Next Computer Revolution (1985), by Howard Rheingold (HTML at rheingold.com)
• Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong A.I. (c2002), ed. by Jay W. Richards, contrib. by Ray Kurzweil, John R. Searle, Michael J. Denton, William A. Dembski, Thomas S. Ray, and George F. Gilder (at kurzweilai.net)
• Principles of Control Systems Engineering (New York et al.: McGraw-Hill Book Co., 1960), by Vincent Del Toro and Sydney R. Parker (page images at HathiTrust)
• The Best of Creative Computing, ed. by David H. Ahl and Burchanel Green
  • volume 1 (1976): page images at atariarchives.org
  • volume 2 (1977): page images at atariarchives.org
  • volume 3 (1980): page images at atariarchives.org

• Lecture Notes on Optimization (electronic edition of "Notes on Optimization", 1998), by Pravin Pratap Varaiya (PDF at Berkeley)
• Unsolved Problems in Mathematical Systems and Control Theory (with problem solutions submitted since first publication in 2004), ed. by Vincent Blondel and Alexandre Megretski (PDF files with commentary at Princeton)
• Control in an Information Rich World: Report of the Panel on Future Directions in Control, Dynamics, and Systems (2002), ed. by Richard M. Murray (PDF files at Caltech)
• Fuzzy Control (c1998), by Kevin M. Passino and Stephen Yurkovich (PDF at Ohio State)

• Lindenmayer Systems, Fractals, and Plants (free electronic edition; c2016), by Przemyslaw Prusinkiewicz and James Hanan
  • PDF at algorithmicbotany.org; site may give security warning
  • PDF at Wayback Machine
• The Algorithmic Beauty of Plants (free electronic edition, c2004), by Przemyslaw Prusinkiewicz and Aristid Lindenmayer
  • PDF files with commentary at algorithmicbotany.org; site may give security warning
  • PDF files with commentary at Wayback Machine

• Graph Representation Learning (prepublication version, 2020), by William L. Hamilton (PDF at McGill)
• Probabilistic Machine Learning for Civil Engineers (prepublication version; 2020), by James-A. Goulet (PDF files with commentary at polymtl.ca)
• Foundations of Machine Learning (second edition; Cambridge, MA and London: MIT Press, c2018), by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (HTML at ublish.com)
• A Course in Machine Learning (online edition; 2017), by Hal Daumé (PDF with commentary at Wayback Machine)
• Elements of Causal Inference: Foundations and Learning Algorithms (Cambridge, MA and London: MIT Press, c2017), by Jonas Peters, Dominik Janzing, and Bernhard Schölkopf (PDF with commentary at MIT Press)
• Machine Learning (main text c1997; additional chapters c2017), by Tom M. Mitchell (PDF with commentary at CMU)
• Deep Learning (prepublication version; 2016), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (illustrated HTML with commentary at deeplearningbook.org)
• Bayesian Reasoning and Machine Learning (free online edition, c2014), by David Barber (PDF with commentary in the UK)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction (second edition, 2009), by Trevor Hastie, Robert Tibshirani, and J. H. Friedman (PDF with commentary at Stanford)
• Reinforcement Learning: An Introduction (first and second editions, 1998 and 2018), by Richard S. Sutton and Andrew G. Barto (HTML and PDF at incompleteideas.net)
• Pattern Recognition and Machine Learning (c2006), by Christopher M. Bishop (PDF at microsoft.com)
• Ethics and Data Science (c2019), by Michael Kosta Loukides, Hilary Mason, and DJ Patil (HTML at oreilly.com)
• Machine Learning, Neural and Statistical Classification, ed. by Donald Michie, D. J. Spiegelhalter, and Charles C. Taylor (PDF and gzipped Postscript in the UK)

More items available under narrower terms.