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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
Lecture Notes On Artificial Intelligence By Dr. Prashanta Kumar Patra
This book covers the following topics: AI Technique, Level of the Model,Problem Spaces, and Search: Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-andTest, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends, Symbolic Reasoning Under Uncertainty, Game Playing, Learning: Rote Learning.
Author(s): Dr. Prashanta Kumar Patra
128 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 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
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 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 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
Artificial Intelligence II (David Marshall)
AI is the part of computer science concerned with designing intelligent computer systems, that is, computer systems that exhibit the characteristics we associate with intelligence in human behaviour - understanding language, learning, reasoning and solving problems .A theme we will develop in this course note is that most AI systems can broken into: Search, Knowledge Representation and applications of the above.
Author(s): David Marshall
NA Pages
Practical Artificial Intelligence Programming in Java
This book is for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical Artificial Intelligence (AI) programming and information processing techniques. Topics covered includes: Search, Reasoning, Semantic Web, Expert Systems, Genetic Algorithms, Neural Networks, Machine Learning with Weka, Statistical Natural Language Processing.
Author(s): Mark Watson
222 Pages
Introduction to Machine Learning
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Author(s):
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