Artificial Intelligence and Engineering Applications

Upon the completion of this course, the student shall be able to: 1. demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations; 2. apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning; 3. demonstrate awareness and good understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models; 4. demonstrate proficiency developing applications in an 'AI language', expert system shell, or data mining tool; and 5. demonstrate proficiency in applying scientific methods to models of machine learning.

Introduction to Artificial Intelligence: Intelligent Agents and Applications of Artificial Intelligence. Knowledge Representation and Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward and Backward chaining, Resolution, Probabilistic reasoning, Utility theory, Hidden Markov Models (HMM), Bayesian Networks. 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. Pattern Recognition: Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parametre 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.