680311 Artificial Intelligence Syllabus PDF

# 680311 Artificial Intelligence and Applications | S. No | Code | Subject Name | Periods per week | Credit | Maximum Marks (Theory Slot) | Maximum Marks (Practical Slot) | Total Marks | |---|---|---|---|---|---|---|---| | 1. | 680311 | Artificial Intelligence and Machine Learning | 3 1 - | 4 | 70...

# 680311 Artificial Intelligence and Applications | S. No | Code | Subject Name | Periods per week | Credit | Maximum Marks (Theory Slot) | Maximum Marks (Practical Slot) | Total Marks | |---|---|---|---|---|---|---|---| | 1. | 680311 | Artificial Intelligence and Machine Learning | 3 1 - | 4 | 70 | 20 | 10 | 100 | ## Objectives: 1. To study the concepts of Artificial Intelligence 2. To learn Methods of solving problems using Artificial Intelligence 3. To present an overview of artificial intelligence (AI) principles and approaches. 4. To introduce the concepts of Expert Systems and machine learning. 5. To have an appreciation for and understanding of both the achievements of AI and the theory underlying those achievements. 6. To have an appreciation for the engineering issues underlying the design of AI systems. 7. To have an understanding of the basic issues of knowledge representation and blind and heuristic search, as well as an understanding of other topics such as minimax, resolution, etc. that play an important role in AI programs. 8. To have a basic understanding of some of the more advanced topics of AI such as learning, natural language processing, agents and robotics, expert systems, and planning. ## UNIT-I ### An Overview of AI: Definitions, Foundations of AI: Philosophy, Mathematics, Psychology, Computer Engineering, linguistics, History of AI, Applications of AI. ## UNIT-II ### AI Production Systems, Search and Control Strategies: AI Production systems and control strategies; Exploring alternatives: Finding a path: Depth first search, hill climbing, breadth first search, beam search, best first search; Finding the best Path: The British Museum search, Branch and Bound Search, A* Search, AO* Search; Game Playing: Minmax search, Alpha-beta pruning, Progressive deepening, Heuristic Pruning. ## UNIT-III ### Knowledge Representations: First order predicate calculus, Clause form representation of WFFs, resolution principle & unification, inference mechanism, semantic networks, frame systems and value inheritance, scripts, conceptual dependency. ## UNIT-IV ### Problem solving by Planning and uncertainty handling and NLP: Components of planning system, Gold Stack Planning, Nonlinear Planning using constraint posting, probability theory, statistical reasoning, fuzzy sets and fuzzy logic, Overview of linguistics, grammars and languages, Parsing techniques ## UNIT-V ### Expert systems and Soft Computing: Introduction and applications of expert systems, Rule-based System Architecture, Non-production system architecture, Expert system shells, Introduction to Some of the AI Techniques like neural networks, genetic algorithms, machine learning, pattern recognition, Robotics etc. ## Books: 1. Introduction to AI and Expert Systems: D.W. Patterson PHI. 2. Artificial Intelligence: P.H. Winston, Addison Wesley. 3. Principles of AI: N.J. Nilsson, Springer-Verlag 4. Artificial Intelligence: Saroj Kaushik, Cengage Learning 5. Artificial Intelligence: A Modern Approach: Stuart Russell and Peter Norvig, Pearson Education ## Course Outcomes: Student would be able to * **CO1:** Demonstrate knowledge of the building blocks of AI as presented in terms of intelligent agents. * **CO2:** Analyze and formalize the problem as a state space, graph, design heuristics and select amongst different search or game based techniques to solve them. * **CO3:** Develop intelligent algorithms for constraint satisfaction problems and also design intelligent systems for Game Playing. * **CO4:** Attain the capability to represent various real life problem domains using logic based techniques and use this to perform inference or planning. * **CO5:** Formulate and solve problems with uncertain information using Bayesian approaches. * **CO6:** Apply concept Natural Language processing to problems leading to understanding of cognitive computing.