Course Description:

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.

Course Modules / Syllabus:

Week 1: Introduction to AI

• History and evolution of AI

• Applications and scope

• Intelligent agents and environments

Week 2: Problem Solving and Search

• Uninformed search (BFS, DFS)

• Informed search (A*, Greedy)

• Constraint satisfaction problems

Week 3: Adversarial Search

• Game theory basics

• Minimax algorithm

• Alpha-beta pruning

Week 4: Knowledge Representation and Reasoning

• Propositional logic

• First-order logic

• Inference methods

Week 5: Planning

• Classical planning

• STRIPS

• Planning graphs

Week 6: Introduction to Machine Learning

• Supervised vs. unsupervised learning

• Regression and classification

• Decision trees and k-NN

Week 7: Neural Networks and Deep Learning

• Perceptrons and activation functions

• Multilayer perceptrons

• Introduction to CNNs and RNNs

Week 8: Natural Language Processing (NLP)

• Text processing and tokenization

• Language models

• Sentiment analysis

Week 9: Robotics and Perception

• Robot localization

• Path planning

• Sensor integration

Week 10: AI in the Real World

• AI in healthcare, finance, and transportation

• Intelligent assistants and recommender systems

Week 11: Ethical and Social Implications of AI

• Bias and fairness

• Job displacement and AI ethics

• Regulation and policy

Week 12: Final Project Presentations

• Students demonstrate their AI systems

• Peer reviews and instructor feedback

Assessment:

• Weekly quizzes and lab assignments (30%)

• Midterm exam (20%)

• Final project (30%)

• Participation and discussions (20%)