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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 
Introduction to Artificial Intelligence by Thomas P Trappenberg
This note covers introduction and history, Search, Robotics and motion planning, Constraint satisfaction problem, Machine learning, Learning machines and the perceptron, Regression, classification and maximum likelihood, Support vector machines, Learning Theory, Generative models and Naïve Bayes, Unsupervised learning, Reinforcement learning, Probabilistic Reasoning, Bayesian networks and Markov models.
Author(s): Thomas P Trappenberg, Dalhousie University
208 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
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 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
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
Building Expert Systems In Prolog (Amzi)
Currently this section contains no detailed description for the page, will update this page soon.
Author(s):
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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
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
Machine Learning, Neural and Statistical Classification
Currently this section contains no detailed description for the page, will update this page soon.
Author(s):
Pages
Introduction to Machine Learning
Currently this section contains no detailed description for the page, will update this page soon.
Author(s):
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