Introduction to Machine Learning

Explorations in Artificial Intelligence and Machine Learning

This PDF covers the following topics related to Artificial Intelligence and Machine Learning : Introduction to Machine Learning,The Bayesian Approach to Machine Learning, A Revealing Introduction to Hidden Markov Models, Introduction to Reinforcement Learning, Deep Learning for Feature Representation, Neural Networks and Deep Learning, AI-Completeness: The Problem Domain of Super-intelligent Machines.

Author(s): Prof. Roberto V. Zicari

178 Pages

Artificial Intelligence by Prof. Pallab Dasgupta and Prof. Partha Pratim Chakrabarti

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

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

Introduction to Artificial Intelligence

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

NA Pages

Techniques of Artificial Intelligence by Vrije Universiteit Brussel

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

NA Pages

Artificial Intelligence Techniques Notes

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

NA Pages

Artificial Intelligence Lecture Notes Veer Surendra Sai University

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

213 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

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

Machine Learning, Neural and Statistical Classification

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Author(s):

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