This course provides a comprehensive introduction to the fundamental concepts and techniques of Artificial Intelligence. Students will explore core topics such as intelligent agents, search algorithms, knowledge representation, reasoning, machine learning, and ethics in AI. Through lectures, practical labs, and projects, learners will gain both theoretical understanding and hands-on experience in building intelligent systems.
History and evolution of AI
Applications and scope
Intelligent agents and environments
Uninformed search (BFS, DFS)
Informed search (A*, Greedy)
Constraint satisfaction problems
Game theory basics
Minimax algorithm
Alpha-beta pruning
Propositional logic
First-order logic
Inference methods
Classical planning
STRIPS
Planning graphs
Supervised vs. unsupervised learning
Regression and classification
Decision trees and k-NN
Perceptrons and activation functions
Multilayer perceptrons
Introduction to CNNs and RNNs
Text processing and tokenization
Language models
Sentiment analysis
Robot localization
Path planning
Sensor integration
AI in healthcare, finance, and transportation
Intelligent assistants and recommender systems
Bias and fairness
Job displacement and AI ethics
Regulation and policy
Students demonstrate their AI systems
Peer reviews and instructor feedback
Weekly quizzes and lab assignments (30%)
Midterm exam (20%)
Final project (30%)
Participation and discussions (20%)