|Artificial Intelligence||Learning Schedule|
Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The main topics in Artificial intelligence include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. In this course, student will study the most fundamental knowledge for understanding Artificial intelligence. Course will introduce some basic search algorithms for problem solving, knowledge representation and reasoning, pattern recognition, fuzzy logic and neural networks.
The objective of this course is to
- learn and possess a firm grounding in the existing techniques and component areas of Artificial Intelligence
- apply this knowledge to the development of Artificial Intelligent Systems and to the exploration of research problems.
On completion of this course, the students will be able to
- understand the principles of problem solving and be able to apply them successfully
- be familiar with techniques for computer-based representation and manipulation of complex information, knowledge, and uncertainty
- gain awareness of several advanced AI applications and topics such as intelligent agents, planning and scheduling, ma-chine learning, etc.
Unit I: Introduction
Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing.
Unit II: Introduction to Search
Searching for solutions, Uniformed search strategies, Informed search strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games, Alpha – Beta pruning.
Unit III: Knowledge Representation & Reasoning
Propositional logic, Theory of first order logic, Inference in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility theory, Hidden Markov Models (HMM), Bayesian Networks.
Unit IV: Machine Learning
Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm, Reinforcement learning.
Unit V: Pattern Recognition
Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parameter estimation methods – Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbour (NN) Rule, Bayes Classifier, Support Vector Machine (SVM), K – means clustering.
- Artificial Intelligence – A Modern Approach – Stuart Russell and Peter Norvig, Pearson Education.
- Artificial Intelligence – Elaine Rich and Kevin Knight, McGraw-Hill
- Introduction to Artificial Intelligence – E Charniak and D McDermott, Pearson Education
- Artificial Intelligence and Expert Systems – Dan W. Patterson, Prentice Hall of India