Lecture Notes in Machine Learning By Zdravko Markov

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

Lecture Notes in Machine Learning By Zdravko Markov

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

65 Pages

Introduction to Artificial Intelligence by Cristina Conati

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

NA Pages

Introduction to Artificial Intelligence Lecture Notes

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

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

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 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

C++ Neural Networks and Fuzzy Logic (V.B. Rao)

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

454 Pages

Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, C. Taylor)

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

NA Pages