Lecture Notes in Machine Learning By Zdravko Markov

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Lecture Notes in Machine Learning By Zdravko Markov

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

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 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

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 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 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.

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.

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

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.

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