Artificial Intelligence
Course Information
- Semester: 6th
- Course Code: 07-0619-AI603
- Credits: 03
- Course Teacher: Md Habibul Basar Faruq
- Email: mh.faruq06@gmail.com
Course Description
This course provides a comprehensive introduction to Artificial Intelligence, covering foundational concepts, algorithms, and applications. Students will explore problem solving, knowledge representation, reasoning, learning, and perception, integrating theory with hands-on programming assignments.
Weekly Topics and Resources
-
Week 1: AI Overview & Foundations
Introduction to AI concepts and history.
Intro Slides (UW CSE573) -
Week 2: Problem Solving & Search
Uninformed and informed search strategies (DFS, BFS, A*).
Search Slides
Informed Search Slides (Annotated) -
Week 3: Adversarial Search & Game Playing
Minimax, Expectimax, and adversarial search strategies.
Adversarial Search Slides (Annotated) -
Week 4: Markov Decision Processes (MDPs)
Modeling decision making under uncertainty.
MDPs Slides (Annotated) -
Week 5: Reinforcement Learning I
Introduction to RL fundamentals and algorithms.
RL I Slides (Annotated) -
Week 6: Reinforcement Learning II
Advanced RL methods and applications.
RL II Slides (Annotated) -
Week 7: Bayesian Networks and Probabilistic Reasoning
Graphical models and inference.
Bayesian Networks Slides (Annotated) -
Week 8: Hidden Markov Models (HMMs)
Sequence modeling and inference techniques.
HMMs Slides (Annotated)
HMM Inference Slides (Annotated) -
Week 9: Inference in Bayesian Networks
Algorithms for probabilistic inference.
BN Inference Slides -
Week 10: Neural Networks and Applications
Fundamentals of neural nets and practical use cases.
Neural Networks Slides -
Weeks 11-14:
Advanced topics, student projects, presentations, and exam preparation. -
Week 15: Final Exam and Project Presentations
Course Credits
- Lecture Slides & Materials: University of Washington CSE573
- Instructor: Md Habibul Basar Faruq
- Contact: mh.faruq06@gmail.com
Suggested Textbooks & References
- Artificial Intelligence: A Modern Approach – Russell & Norvig
- Artificial Intelligence – Patrick H. Winston
- Additional research papers and online resources will be provided throughout the course.
Assessment & Evaluation
- Programming Assignments & Projects
- Midterm & Final Examinations
- Class Participation and Presentations