CSPC-414 Artificial Intelligence in Engineering
Teaching Scheme			Credit	Marks Distribution			Duration of End Semester Examination
L	T	P		Internal Assessment	End Semester Examination	Total
3	0	0	3	Maximum Marks: 40	Maximum Marks: 60	100	3 Hours
				Minimum Marks: 16	Minimum Marks: 24	40

Unit-I

Fundamentals of Artificial Intelligence (AI): Introduction to AI, History of AI, General applications of AI, Need of AI in Engineering, Problem solving, Process of problem solving, breadth first search, depth first search, heuristics search techniques, best first search, Introduction to intelligent systems, Various approaches to AI: Cybernetics and brain simulation, Symbolic, Sub-symbolic, Statistical.

Ethical and Social Implications of AI: Ethical considerations in AI development and deployment, Impact of AI on jobs and society, Regulatory and policy issues.

Unit-II

Fundamentals of Machine Learning (ML): Introduction to Machine Learning, datasets, Forms of Learning: Supervised and Unsupervised Learning, reinforcement learning, processes involved in Machine Learning, Applications of ML in Engineering.

Data Preprocessing, cleaning and normalization Approaches in Machine Learning (ML): Data preprocessing, Data cleaning, Feature selection and extraction, Data normalization and scaling.

Unit-III

Artificial Neural Networks: Introduction to Artificial Neural Networks (ANNs): Definition and history of ANNs, Types of ANNs architectures, Basic architecture of ANNs, Activation functions, Singled-Layered and Multi-Layered Perceptron, Backpropagation algorithms, Applications of ANNs in Engineering.

Unit-IV

Fuzzy Logic and Genetic Algorithm: Introduction to Fuzzy Logic: Basic concepts, history, and fuzzy set theory. Processes in a fuzzy logic system, Applications of Fuzzy Logic in Engineering.

Genetic Algorithm (GA): Basics of GA, Main operations of GA, Flowchart of GA, Working principle of GA in step by step, Applications in Engineering.