Machine Learning Advanced Techniques and Emerging Applications

Machine Learning Advanced Techniques and Emerging Applications

This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

Author(s): Hamed Farhadi

NA Pages

Artificial Intelligence by Seoul National University

This book explains the following topics: History of AI, Machine Evolution, Evolutionary Computation, Components of EC, Genetic Algorithms, Genetic Programming, Uninformed Search, Search Space Graphs, Depth-First Search, Breadth-First Search, Iterative Deepening, Heuristic Search, The Propositional Calculus, Resolution in the Propositional Calculus, The Predicate Calculus, Resolution in the Predicate Calculus, Reasoning with Uncertain Information, Agent Architectures.

Author(s): Seoul National University

NA 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

Introduction to Artificial Intelligence

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.

Author(s): Vincent Conitzer

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

Techniques in Artificial Intelligence

This note provides an introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.

Author(s): Prof. Tomas Lozano-Perez and Prof. Leslie Kaelbling

NA Pages

Artificial Intelligence Lecture Notes MIT

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

NA 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

Introduction to Machine Learning (N. Nilsson)

This note covers the following topics: Preliminaries, Boolean Functions, Using Version Spaces for Learning, Statistical Learning, Decision Trees, Inductive Logic Programming , Computational Learning Theory, Unsupervised Learning, Temporal-Difference Learning, Delayed-Reinforcement Learning.

Author(s): Nils J. Nilsson

NA Pages