This book covers the following topics: Introduction to Statistics, General Machine Learning Strategies, Linear Algebra, Regression Models, Causality, Assessing model goodness, Machine Learning Models, Different Types of Data, Machine Learning T echniques, Unsupervised Learning, Applications, Responsible Data Science
Author(s): Ott Toomet, Information School, University of Washington
437Pages
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
65Pages
This book covers the following topics: AI Technique, Level of the Model,Problem Spaces, and Search: Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-andTest, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends, Symbolic Reasoning Under Uncertainty, Game Playing, Learning: Rote Learning.
Author(s): Dr. Prashanta Kumar Patra
128Pages
This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.
Author(s): Hamed Farhadi
NAPages
This note covers the following topics: Atlas: humanoid robot, VoiceTra Real-Time Machine Translation, DeepMind&
Author(s): Prof. Bojana Dalbelo Basic and Assoc. Prof. Jan Snajder
79Pages
This note covers the following topics: Introduction to Artificial Intelligence, State Space, Representation and Search, Prolog Introduction, Lists, Predicates and Relations, IO, Arithmetic and Control flow, Recursion with Examples, Games, Heuristics, Game Playing, Knowledge Representation, Threshold Logic Units and Artificial Neural networks.
Author(s): Prof. Hugh Murrell
NAPages
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
NAPages
This note covers the following topics: Problem Solving by Search, Informed State Space Search, Propositional Logic, Informed State Space Search, AND/OR Graphs and Game Trees, Method of Resolution Refutation, GraphPLAN and SATPlan, Reasoning under Uncertainty, Learning Decision Trees, Convolutional and Recurrent Neural Networks.
Author(s): Prof. Pallab Dasgupta and Prof. Partha Pratim Chakrabarti
NAPages
This note provides an introduction to the field of artificial intelligence. Major topics covered includes: reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.
Author(s): Cristina Conati
NAPages
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
NAPages
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
NAPages
This note explains the following topics: Search, Game playing, Logic, Planning, Probabilistic reasoning, Decision theory, Markov decision processes, POMDPs, Game theory, Machine learning, Wrapping up.
Author(s): Vincent Conitzer
NAPages
This note explains the following topics: State Space Search, Decision Trees, Evaluating Hypotheses, Evaluation of hypothesis, Neural Networks, Computational Learning Theory, DMF Clustering, Data Mining, Text Mining, Graph Mining, Text Mining.
Author(s): Vrije Universiteit Brussel
NAPages
This note provides a general introduction to artificial intelligence and its techniques. Topics covered includes: Biological Intelligence and Neural Networks, Building Intelligent Agents, Semantic Networks, Production Systems, Uninformed Search, Expert Systems, Machine Learning, Limitations and Misconceptions of AI.
Author(s): Dr John A. Bullinaria
NAPages
This note is designed as a broad rather than in-depth introduction to the principles of artificial intelligence, its characteristics, major techniques, and important sub-fields and applications.
Author(s): Professor Yun Peng
NAPages
This lecture note covers the following topics: Formalized symbolic logic, Probabilistic Reasoning Structured knowledge, graphs, frames and related structures, Matching Techniques, Knowledge organizations, Management, Natural Language processing, Pattern recognition, expert systems.
Author(s): Prof. Pradipta Kumar Das, Prof. D. Chandrasekhar Rao and Prof. Kishore Kumar Sahu
213Pages
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
NAPages
This lecture note covers the following topics: Introduction to Agent, Problem Solving using Search, State Space Search, Pegs and Disks problem, Uninformed Search , Single agent search, Informed Search Strategies, Two agent, Constraint satisfaction problems, Knowledge Representation and Logic, First Order Logic, Rule based Systems, Other representation formalisms, Planning, Reasoning with Uncertainty - Probabilistic reasoning, Reasoning with uncertainty-Fuzzy Reasoning.
Author(s): IIT Kharagpur
NAPages
This note explains artificial intelligence, including agent design, heuristic search, knowledge representation, planning, logic, natural language processing and machine learning.
Author(s): Ray Toal
NAPages
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
NAPages
This course note covers major topics of AI, including Search, Logic and Knowledge Representation, and Natural Language Processing, with brief coverage of the Brain and Machine Vision.
Author(s): Gordon S. Novak Jr
NAPages
This note covers the following topics: Search, Backtracking Search, Game Tree Search, Reasoning Under Uncertainty, Planning, Decision Making under Uncertainty.
Author(s): Fahiem Bacchus
NAPages
This book covers the following topics: Introduction to Type Theory, Statements and Definitions in Nuprl, Proofs, Computation, Proof Tactics and System Description.
Author(s): Richard Eaton
NAPages
This book explains the theory of neural networks and provides illustrative examples in C++ that the reader can use as a basis for further experimentation.
Author(s): Valluru B. Rao
454Pages
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Author(s):
Pages
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
NAPages
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
NAPages
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
NAPages
This book covers the following topics: Introduction to Type Theory, Statements and Definitions in Nuprl, Proofs, Proof Tactics, System Description, The Rules, The Metalanguage, Building Theories, Recursive definition.
Author(s): Computing and Information Science, Cornell University
NAPages
This book is for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical Artificial Intelligence (AI) programming and information processing techniques. Topics covered includes: Search, Reasoning, Semantic Web, Expert Systems, Genetic Algorithms, Neural Networks, Machine Learning with Weka, Statistical Natural Language Processing.
Author(s): Mark Watson
222Pages
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Author(s):
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Author(s):
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Author(s):
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Author(s):
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