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 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 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.
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.
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.
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 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