Lecture Notes for Machine Learning and Data Science Courses

Lecture Notes for Machine Learning and Data Science Courses

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

437 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

Artificial Intelligence by Professor Yun Peng

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

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 by IIT Kharagpur

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

NA Pages

Artificial Intelligence Lectures slides and readings

This note covers the following topics: Search, Backtracking Search, Game Tree Search, Reasoning Under Uncertainty, Planning, Decision Making under Uncertainty.

Author(s): Fahiem Bacchus

NA Pages

Implementing Mathematics with The Nuprl Proof System

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

NA Pages

Practical Artificial Intelligence Programming in Java

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

222 Pages

Building Expert Systems In Prolog

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Author(s):

Pages