Artificial Intelligence — Lecture Notes and Course Materials
This page provides open educational resources for the study of Artificial Intelligence, including lecture material, conceptual explanations, algorithms, examples, and supporting learning resources. The material is intended for undergraduate and postgraduate students, teachers, researchers, and independent learners of Computer Science and Artificial Intelligence.
The resources have been developed from material used in teaching Artificial Intelligence and related areas of Computer Science. They are presented here as a freely accessible academic learning resource for anyone interested in the foundations, methods, applications, and continuing development of Artificial Intelligence.
The material is based substantially on the textbook Fundamentals of Artificial Intelligence, published by Springer Nature, and follows its broad organization of topics and chapters.
What You Will Find
- Foundations and principles of Artificial Intelligence
- Knowledge representation and automated reasoning
- Logic programming and rule-based systems
- State-space and heuristic search
- Constraint satisfaction and game playing
- Reasoning under uncertainty
- Machine learning and statistical learning
- Automated planning and intelligent agents
- Data mining and information retrieval
- Natural Language Processing and speech recognition
- Computer vision and machine vision
- References and links to related scholarly resources
Course Information
Prerequisites
- Programming in C, C++, or Python
- Discrete Mathematical Structures
- Data Structures and Algorithms
- Probability and Statistics — desirable
Textbook
Fundamentals of Artificial Intelligence
Course Description
Artificial Intelligence is a major area of Computer Science concerned with the design of systems capable of performing tasks that require reasoning, learning, perception, planning, language understanding, decision making, and intelligent problem solving.
The course introduces the theoretical foundations and computational methods used to construct intelligent systems. Topics include knowledge representation, logic and reasoning, search, machine learning, planning, intelligent agents, data mining, natural language processing, speech recognition, and computer vision.
The material combines mathematical foundations with algorithms, worked examples, conceptual explanations, and applications, providing a foundation for further study and research in Artificial Intelligence.
Learning Outcomes
- Understand the fundamental principles and foundations of Artificial Intelligence.
- Represent knowledge using logic, rules, networks, and other formal structures.
- Design and analyze intelligent search and reasoning algorithms.
- Understand supervised, unsupervised, and reinforcement learning approaches.
- Apply planning, data mining, NLP, speech recognition, and computer vision methods.
- Understand intelligent-agent architectures and multi-agent systems.
- Develop the theoretical background required for advanced study and research in Artificial Intelligence and related fields.
Course Contents
The course provides a comprehensive introduction to Artificial Intelligence, beginning with its foundations, knowledge representation, logical reasoning, and search techniques. It then progresses through machine learning, planning, intelligent agents, data mining, information retrieval, natural language processing, speech recognition, and computer vision.
The material is organized into topic modules corresponding to the chapters of the Springer Nature textbook Fundamentals of Artificial Intelligence. The resources can support university teaching, faculty development, self-learning, and further academic study.
Lecture Notes and Course Modules
Module 1: Foundations of Artificial Intelligence
Chapter 1: Introduction to Artificial Intelligence
Introduction to Artificial Intelligence, intelligent systems, history, goals of AI, the Turing Test, symbol systems, knowledge representation, engineering applications, and future directions of Artificial Intelligence.
Module 2: Logic and Automated Reasoning
Chapter 2: Logic and Reasoning Patterns
Logical foundations of AI including propositional logic, syntax, semantics, reasoning, semantic tableaux, normal forms, resolution, and non-monotonic reasoning.
Chapter 3: First Order Predicate Logic
Predicate logic, quantifiers, clause forms, unification, resolution theorem proving, Herbrand universe, most general unifier, and knowledge representation.
Module 3: Rule-Based Systems and Logic Programming
Chapter 4: Rule-Based Reasoning
Forward and backward chaining, inference engines, rule selection, model-based reasoning, case-based reasoning, and expert systems.
Chapter 5: Logic Programming and Prolog
Logic programming, facts, rules, recursion, control strategy, Prolog interpreters, and Artificial Intelligence programming examples.
Module 4: Knowledge Representation
Chapter 6: Real-World Knowledge Representation and Reasoning
Taxonomies, ontologies, commonsense reasoning, default reasoning, action and change, situation calculus, and real-world knowledge.
Chapter 7: Networks-Based Representation
Semantic networks, conceptual graphs, frames, description logic, conceptual dependency, and scripts.
Module 5: State Space and Heuristic Search
Chapter 8: State Space Search
State-space representation, breadth-first search, depth-first search, iterative deepening, bidirectional search, and complexity analysis.
Chapter 9: Heuristic Search
Hill climbing, best-first search, A* search, simulated annealing, genetic algorithms, and heuristic optimization.
Module 6: Constraint Satisfaction and Game Playing
Chapter 10: Constraint Satisfaction Problems
Constraint graphs, backtracking, constraint propagation, generate-and-test algorithms, cryptarithmetic, and CSP applications.
Chapter 11: Adversarial Search and Game Theory
Game theory, minimax search, alpha-beta pruning, game strategies, zero-sum games, and adversarial search.
Module 7: Reasoning Under Uncertainty
Chapter 12: Reasoning in Uncertain Environments
Probability theory, Bayesian reasoning, Bayesian networks, Dempster-Shafer theory, fuzzy sets, and fuzzy inference.
Module 8: Machine Learning
Chapter 13: Machine Learning
Learning systems, supervised and unsupervised learning, decision trees, inductive learning, reinforcement learning, analogy, explanation-based learning, and applications.
Chapter 14: Statistical Machine Learning
Support Vector Machines, k-nearest neighbours, Naive Bayes classifiers, artificial neural networks, deep learning, and instance-based learning.
Module 9: Automated Planning and Intelligent Agents
Chapter 15: Automated Planning
Classical planning, STRIPS, planning graphs, partial-order planning, hierarchical planning, planning languages, and multi-agent planning.
Chapter 16: Intelligent Agents
Agent architectures, autonomous agents, mobile agents, communication languages, coordination, cooperation, and coalition formation.
Module 10: Data Mining
Chapter 17: Data Mining
Knowledge discovery from large databases, classification, clustering, association rules, sequential pattern mining, and scientific applications.
Module 11: Information Retrieval and Web Intelligence
Chapter 18: Information Retrieval
Vector space models, probabilistic retrieval, indexing, query expansion, semantic web, distributed information retrieval, and Bayesian methods.
Module 12: Natural Language Processing and Speech Recognition
Chapter 19: Natural Language Processing
Natural language understanding, grammars, parsing, information extraction, question answering, commonsense reasoning, and NLP tools.
Chapter 20: Automatic Speech Recognition
Speech recognition processes, language and acoustic models, Hidden Markov Models, Kaldi, CMU-Sphinx, HTK, and DeepSpeech.
Module 13: Computer Vision
Chapter 21: Machine Vision
Image understanding, computer vision, object recognition, 3-D reconstruction, object tracking, robot vision, and vision applications.
About These Lecture Notes
These lecture notes and course materials are based on material developed and used while teaching Artificial Intelligence and related subjects in Computer Science and Engineering. They have been organized and made available as an open educational resource for students, teachers, researchers, and independent learners.
The material is intended to complement standard textbooks and classroom instruction. Readers are encouraged to consult the recommended textbook, research literature, and other scholarly sources for deeper study.
Artificial Intelligence and Related Areas
Artificial Intelligence is a broad and rapidly evolving field that intersects with many areas of Computer Science. The topics represented in these materials include classical AI as well as areas that have developed into major contemporary fields of research and application.
- Knowledge Representation and Reasoning
- Machine Learning
- Natural Language Processing
- Speech Recognition
- Computer Vision
- Intelligent Agents
- Planning and Decision Making
- Data Mining and Information Retrieval
- Logic Programming
- Search and Optimization
My broader academic and research interests also extend to emerging areas of Artificial Intelligence and Computer Science, including contemporary developments in machine learning, natural language technologies, generative AI, and large language models.
Further Learning
The textbook Fundamentals of Artificial Intelligence provides a detailed treatment of the topics covered on this page, including theoretical foundations, algorithms, examples, illustrations, review questions, and exercises.