CS 441 Introduction to Artificial Intelligence

CS441: Introduction to Artificial Intelligence

Classes: Wednesday 14.30 – 17.30 pm


Instructor: Dr. Şadi Evren ŞEKER (+9 0531 605 6726)

E-Mail: ai@sadievrenseker.com

Web Site: http://sadievrenseker.com/wp/?page_id=2237

Course Content:

  • History and Philosophy of the Artificial Intelligence (AI)
  • Classical AI approaches like search problems, machine learning, constraint satisfaction, graphical models, logic etc.
  • Learning how to model a complex real-world problem by the classical AI approach


  • Introduction to Artificial Intelligence Problems
  • Programming with Python for solving Real Life problems with AI Algorithms
  • Writing a real world application with an AI module (like a game)
  • Introducing sub-AI topics like neural computing, uncertainity and bayesian networks, concept of learning (supervised / unsupervised) etc.


  • S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall
  • —A must check : http://aima.cs.berkeley.edu
  • Another useful link: https://www.cse.wustl.edu/~garnett/cse511a/
  • Some parts of the course is related to Machine Learning, Data Science, Data Mining, Pattern Recognition, Natural Language Processing, Statistics, Logic, Artificial Neural Networks and Fuzzy Logic, so you can read any [text] books about the topics.

—Grading:  Final Exam (50%),  Final Project (50%)

Final Exam date will be announced at the end of the semester, during the final exams week.


Final Project will be group work (max 3 people in a group), and expectations are : Project Report, Project Presentation, Running Code in Python.

Projects should include at least 2 implementation from following list:  Search/ Heuristic, CSP, Game Trees, Logic, Fuzzy, Machine Learning / ANN

Late Submission Policy: Submissions are due to Dec 11, Class time (14.30) any late submission will get 10% penalty for each day. Also Presentations will start at Dec 11, 14:30 in random order. If you can not present the project on first week than another 20% penalty will apply.

Course Outline:

  • —Introduction and Agents (chapters 1,2)
  • —Search (chapters 3,4,5,6)
  • —Logic (chapters 7,8,9)
  • —Planning (chapters 11,12)
  • —Uncertainty (chapters 13,14)
  • —Learning (chapters 18,20)
  • —Natural Language Processing (chapter 22,23)

Schedule and Contents (Very Very Very Tentative):

  • Class 1, Sep 18 :[PPT] Introduction : Course Demonstration Slides, Introduction Slides
  • Class 2, Sep 25: [PPT] Agents
  • Class 3, Oct 2:  [PPT] Search
  • Class 4, Oct 9: No Class
  • Class 5, Oct 16: [PPT] Heuristic Search
  • Class 6, Oct 23: [PPT]Constraint Satisfaction Problems
  • Class 7, Oct 30: [PPT] Game Playing
  • Class 8, Nov 6: Constraint Satisfaction Problems (CSP)
  • Class 9, Nov 13: [PPT] Logic, [PPT]First Order Logic, Inference in First Order Logic, [PPT] Uncertainity and Fuzzy Logic.
  • Class 10, Nov 20: Supervised / Unsupervised Learning and Classification / Clustering Problems, k-nn, Decision Tree, Random Forest, Logistic Regression
  • Class 11, Nov 27: Regression : Logistic Regression, Decision Tree Regression, Linear Regression, Polynomial Regression
  • Class 12, Dec 4:  [PPT] Artificial Neural Networks
  • Class 13, Dec 11: Project Presentations
  • Class 15, Dec 18: Project Presentations
  • Class 16, Dec 25: No Class. [PPT] Genetic Algorithms
  • Final Exam : Date TBA

Collaboration Policy: You may freely use internet resources and your course notes in completing assignments and quizzes for this course. You may not consult any person other than the professor when completing quizzes or exams. (Clarifying questions should be directed to the professor.) On assignments you may collaborate with others in the course, so long as you personally prepare the materials submitted under your name, and they accurately reflect your understanding of the topic. Any collaborations should be indicated by a note submitted with the assignment.


Please fill the knowledge card attached here, and send it back via email.