CSE-315 Artificial Intelligence



E-mail: atik@cse.green.edu.bd

🕾 Mob. +8801912961096

:office: Room: A-510 Desk No. : 08



Class Routine – Summer 2026 Semester


Day 08:30-10:00 10:00-11:30 11:30-1:00 Break 1:30-03:00 3:00-4:30
Sat CSE 435
223_D1
K-105		 CSE 435
223_D1
K-105		     Tutor Time Research Meeting
Sun   CSE 403
232_D2
K-105		 Tutor Time   CSE 315
241_D2
K-108		 Research Meeting
Mon CSE 404
232_D4
L-106		 CSE 404
232_D4
L-106		 Research Meeting   CSE 310
Old Batch
A-502		 CSE 310
Old Batch
A-502
Tue CSE 315
241_D2
K-108		 CSE 403
232_D2
K-105		 Research Meeting   Tutor Time Tutor Time
Wed            



Topic Outline

Lecture Selected Topic Article / Materials Problems
(1) Socialization and Introduction to the Course Class Notes, Slides  
(2–3) What is AI? Real-World Impact of AI, Rational Decisions, History of AI, What Can AI Do?, Designing Rational Agents, Reflex Agents, Planning Agents Class Notes, Slides CT 1
(4–5) Uninformed Search: Search Problems, Depth First Search, Search Algorithm Properties, Breadth-First Search, Iterative Deepening, Uniform Cost Search, Search and Models Class Notes, Slides  
(6–8) Informed Search: Heuristics, Greedy Search, A* Search, Graph Search Class Notes, Slides Assignment 1
(9–11) Constraint Satisfaction Problems: Constraint Propagation, Backtracking Search, Forward Checking Class Notes, Slides  
(12) Local Search: Hill Climbing, Genetic Algorithms Class Notes, Slides  
  Midterm Examination    
(13–14) Adversarial Search: Game Playing Overviews, The Minimax Search Procedure, Adding Alpha-Beta Cut-offs Class Notes, Slides  
(15–17) Uncertainty: Probability, Conditional Probability, Random Variables, Independence, Bayes’ Rule, Joint Probability, Bayesian Networks, Sampling, Markov Models, Hidden Markov Models Class Notes, Slides CT 2
(18–19) Fuzzy Logic: Fuzzy Expert Systems, Fuzzy Sets, and Building Fuzzy Rule-Based Systems Class Notes, Slides  
(20–22) Reinforcement Learning: Introduction to Reinforcement Learning; Key Concepts (Agent, Environment, State, Action, Reward); The Reinforcement Learning Problem; Types of Reinforcement Learning; Model-Based vs. Model-Free Learning; Exploration vs. Exploitation; Markov Decision Processes (MDPs); Policy, Value Function, and Q-Function Class Notes, Slides Assignment 2
(23–24) AI Ethics, Responsible AI, Explainable AI (XAI), AI Alignment Problem Class Notes, Slides Presentation
  Final Examination    



  1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Edition.
  2. Elaine Rich and Kevin Knight, Artificial Intelligence.
  3. Dan W. Patterson, Introduction to Artificial Intelligence and Expert Systems.
  4. Class Notes, Slides, and Supplementary Reading Materials.