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Lecture Notes in Machine Learning By Zdravko Markov
This book contains the following topics: Concept learning, Languages for learning, Version space learning, Induction of Decision Trees, Covering strategies, Inductive Logic Programming, Bayesian approach and MDL, Unsupervised Learning,Paradigms, Learning semantic nets, Induction task, Relational languages, Language of logic programming, Search strategies in version space, Candidate Elimination Algorithm, Representing disjunctive concepts, Building a decision tree, Learning multiple concepts, Learning from noisy data, Basic idea, Basic idea, Searching the space of propositional hypotheses, Searching the space of relational hypotheses, ILP task, Ordering Horn clauses, Inverse Resolution, Predicate Invention, Extralogical restrictions, Illustrative examples, Basic strategies for solving the ILP problem, Bayesian induction, Occams razor, Evaluating propositional hypotheses, Evaluating relational hyporheses, Introduction, COBWEB, Introduction, Basic concepts of EBL, Example and Discussion
Author(s): Zdravko Markov
65 Pages 
Introduction to Artificial Intelligence by Thomas P Trappenberg
This note covers introduction and history, Search, Robotics and motion planning, Constraint satisfaction problem, Machine learning, Learning machines and the perceptron, Regression, classification and maximum likelihood, Support vector machines, Learning Theory, Generative models and Naïve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.
Author(s): Thomas P Trappenberg, Dalhousie University
208 Pages
Artificial Intelligence by Seoul National University
This book explains the following topics: History of AI, Machine Evolution, Evolutionary Computation, Components of EC, Genetic Algorithms, Genetic Programming, Uninformed Search, Search Space Graphs, Depth-First Search, Breadth-First Search, Iterative Deepening, Heuristic Search, The Propositional Calculus, Resolution in the Propositional Calculus, The Predicate Calculus, Resolution in the Predicate Calculus, Reasoning with Uncertain Information, Agent Architectures.
Author(s): Seoul National University
NA Pages
Introduction to Artificial Intelligence Lecture Notes
This book explains the following topics: Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning, Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.
Author(s): Chuck Dyer
NA Pages
Artificial Intelligence Lecture Materials
This note will provide an introduction to the field of Artificial Intelligence. It will cover a number of AI ideas and techniques, as well as give you a brief introduction to symbolic computing.
Author(s): Diane Cook
NA Pages
Techniques in Artificial Intelligence
This note provides an introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.
Author(s): Prof. Tomas Lozano-Perez and Prof. Leslie Kaelbling
NA Pages
Artificial Intelligence Lecture Notes MIT
This course note introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view.
Author(s): Prof. Leslie Kaelbling and Prof. Tomas Lozano-Perez
NA Pages
Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, C. Taylor)
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. It provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems.
Author(s): D. Michie, D.J. Spiegelhalter, C.C. Taylor
NA Pages
Introduction to Machine Learning (N. Nilsson)
This note covers the following topics: Preliminaries, Boolean Functions, Using Version Spaces for Learning, Statistical Learning, Decision Trees, Inductive Logic Programming , Computational Learning Theory, Unsupervised Learning, Temporal-Difference Learning, Delayed-Reinforcement Learning.
Author(s): Nils J. Nilsson
NA Pages
Artificial Intelligence II (David Marshall)
AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems .A theme we will develop in this course note is that most AI systems can broken into: Search, Knowledge Representation and applications of the above.
Author(s): David Marshall
NA Pages
Introduction to Machine Learning
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