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
This note describes the following topics: introduction to AI and production systems,
Representation of Knowledge, Knowledge Representation using predicate logic,
Knowledge Inference, Planning and Machine Learning, Expert Systems and Meta
Knowledge.
Author(s): St Anne College of Engineering and
Technology
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.
This note covers the
following topics: Problem Solving by Search, Informed State Space Search,
Propositional Logic, Informed State Space Search, AND/OR Graphs and Game Trees,
Method of Resolution Refutation, GraphPLAN and SATPlan, Reasoning under
Uncertainty, Learning Decision Trees, Convolutional and Recurrent Neural
Networks.
Author(s): Prof.
Pallab Dasgupta and Prof. Partha Pratim Chakrabarti
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 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 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.
This note covers the following topics: Search, Backtracking
Search, Game Tree Search, Reasoning Under Uncertainty, Planning, Decision Making
under Uncertainty.
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.