Digital notes on Artificial Intelligence
This lecture note covers topics starting with an introduction to AI and progressing through various search strategies and A* search. The text delves into challenges such as searching with partial observations and constraint satisfaction problems, introducing techniques like Alpha Beta Pruning. It explores reasoning methods like forward and backward chaining, syntax and semantics of First-Order Logic, knowledge engineering, resolution, classical planning, and planning with state space search.it also handles with topics like acting in nondeterministic domains and multi-agent planning, Bayes Rule, Bayesian Networks and Dempster Shafer Theory.It includes learning decision trees and the role of knowledge in learning.
Author(s): Department of Information Technology , Malla Reddy College Of Engineering and Technology
143 Pages
Explorations in Artificial Intelligence and Machine Learning
This PDF covers the following topics related to Artificial Intelligence and Machine Learning : Introduction to Machine Learning,The Bayesian Approach to Machine Learning, A Revealing Introduction to Hidden Markov Models, Introduction to Reinforcement Learning, Deep Learning for Feature Representation, Neural Networks and Deep Learning, AI-Completeness: The Problem Domain of Super-intelligent Machines.
Author(s): Prof. Roberto V. Zicari
178 Pages
Introduction to Artificial Intelligence by Bojana Dalbelo Basic and Jan Snajder
This note covers the following topics: Atlas: humanoid robot, VoiceTra Real-Time Machine Translation, DeepMind&
Author(s): Prof. Bojana Dalbelo Basic and Assoc. Prof. Jan Snajder
79 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
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 Course Notes
This note explains artificial intelligence, including agent design, heuristic search, knowledge representation, planning, logic, natural language processing and machine learning.
Author(s): Ray Toal
NA Pages
Artificial Intelligence Lecture Notes
This course note covers major topics of AI, including Search, Logic and Knowledge Representation, and Natural Language Processing, with brief coverage of the Brain and Machine Vision.
Author(s): Gordon S. Novak Jr
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
Building Expert Systems In Prolog
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Introduction to Machine Learning
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