Artificial Intelligence Applications

Course Objectives :

1. To acquire knowledge on intelligent systems and agents
2. To understand fundamental concepts of machine learning and learning techniques
3. To understand the fundamental concepts and techniques of natural language processing (NLP)
4. Familiarize with soft computing concepts, introduce and use the idea of Neural networks, fuzzy logic and use of heuristics based on human experience

Course Outcomes (CO):

• CO 1 Understand intelligent agents, search techniques and apply various problem solving strategies to common AI applications
• CO 2 Understand basic concepts of machine learning, its goals and applications.
• CO 3 Understand the algorithmic approach to NLP
• CO 4 Understand basic principles, techniques, and applications of soft computing

UNIT-I

Introduction: Introduction to intelligent agents Problem solving: Problem formulation, uninformed search strategies, heuristics, informed search strategies, constraint satisfaction Solving problems by searching, state space formulation, depth first and breadth first search, iterative deepening

UNIT-II

Basic concepts: Definition of learning systems, Goals and applications of machine learning. Aspects of developing a learning system: training data, concept representation, function approximation.

Types of Learning: Supervised learning and unsupervised learning. Overview of classification: setup, training, test, validation dataset, over fitting.

Classification Families: linear discriminative, non-linear discriminative, decision trees, probabilistic (conditional and generative), nearest neighbor

UNIT-III

Introduction to NLP: Characteristics of Natural Language, Language structure, Sentence Structure, Language analyzer, Lexicon, word formation, Morphology, syntax analysis (parsing), semantics, ambiguity, pragmatics and discourse

UNIT-IV

Introduction of soft computing, soft computing vs. hard computing, various types of soft computing techniques, Neural Computing, Fuzzy Computing, Genetic Algorithms, Associative Memory, Adaptive Resonance Theory, Classification, Clustering, Bayesian Networks, Probabilistic reasoning, Various applications of Soft Computing.

Textbook(s):

1. Rich and Knight, “Artificial Intelligence”, Tata McGraw Hill, 1992
2. Tom M Mitchell, Machine Learning, McGraw Hill Education
3. Chaitanya, Vineet, Rajeev Sangal, and Akshar Bharati. “Natural language processing: A Paninian perspective”. Prentice-Hall of India, 1996.
4. S.N.Sivanandam, S.N.Deepa, “Principles of Soft Computing “, Wiley India, 2007

References:

1. S. Russel and P. Norvig, “Artificial Intelligence — A Modern Approach”, Second Edition, Pearson Edu
2. Introduction to Machine Learning - Ethem Alpaydin, MIT Press, Prentice hall of India.
3. Syal, Pushpinder, and DharamVir Jindal. “An introduction to linguistics: Language, grammar and semantics” PHI Learning Pvt. Ltd., 2007.
4. S. Rajasekaran, G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.