Lecture Notes for Machine Learning and Data Science Courses

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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

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

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

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.

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.

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.

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

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

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.