CS362 Introduction to Artificial Intelligence

CS362: Introduction to Artificial Intelligence

Classes: Tue 09.30 – 12.30 am


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

E-Mail: ai@sadievrenseker.com

Web Site: http://sadievrenseker.com/wp/?page_id=2258

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 a mathematical notation language (like a lisp variant, scheme)
  • 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
  • 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: Individual Term Project %100

Project Requirements :

You are free to select a project topic. The only requirement about the project is, you have to cover at least two topics from the following list and solve the same problem with two separate approaches from the list, you are also asked to compare your findings from these two alternative solutions : Search/ Heuristic, CSP, Game Trees, Logic, Fuzzy, Machine Learning / ANN

Example project topic: chess playing AI can be developed with CSP, search or machine learning, so you can propose a project to develop an AI for chess playing and you can solve the problem with game trees and machine learning, just as an example.

Project proposal : until Apr 30 : please explain your project idea and alternative solution approaches from the course content.

Project Deliverables: You are asked to submit the below items via mail until May 19, 2020. (updated deadline: Jun 09,  2020 also another important update about the regulations is : you have to submit your projects both in OYS (oys.akdeniz.edu.tr) and send through the course email address)

  1. Presentation and Demo video: please shoot a video for your presentation and demo of your project.
  2. Project Presentation: slides you are using during the presentation
  3. Project Report : a detailed explanation of your approaches, the difficulties you have faced during the project implementation, comparison of your two alternative approaches to the same problem (from the perspectives of implementation difficulties, their success rates, running performances etc.), some critical parts of your algorithms
  4. Running Code: you are free to implement your solution in any platform / language. The only requirement about your implementation is, you have to code the two alternative solution on the same platform / programming language (otherwise it will not be fair to compare them). Please also provide an installation manual for your platform and running your code.
  5. Interview: A personal interview will be held after the submissions. Each of you will be asked to provide a time slot of at least 30 minutes for your projects. During this time, you will be asked to connect via an online platform and show your running demo and answer the questions. Please also attach your available time slots to your submissions.

Project Policies: There will be no late submission policy. If you can solve a problem with only 1 approach, which also means you can not compare two approaches, will be graded with 35 points over 100 max. So, please push yourselves to submit two separate approaches for your problem. You are free to use any library during your projects, you are not allowed to use a library or any code on the internet or written by anybody else on the AI part of your project only. So, in other words, you have to write the two different AI module for your project with two different approaches from the course content and using somebodyelse’s code in the AI module will get 0 as the final grade.

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, Feb 18 :[PPT] Introduction : Course Demonstration Slides, Introduction Slides
  • Class 2, Feb 25: [PPT] Agents
  • Class 3, Mar 3:  [PPT] Search
  • Class 4, Mar 10: [PPT] Heuristic Search
  • Class 5, Mar 17: [PPT]Constraint Satisfaction Problems
  • Class 6, Mar 24:  [PPT] Game Playing
  • Class 7, Mar 31:  [PPT] Logic
  • Class 8, Apr 7:  [PPT]First Order Logic, Inference in First Order Logic
  • Class 9, Apr 14: [PPT] Uncertainity and Fuzzy Logic
  • Class 11, Apr 21: Machine Learning, Big Data, Data Science Concepts
  • Class 12, Apr 28:Supervised Learning, Classification and Prediction
  • Class 13, May 5: Unsupervised Learning : Clustering, Association Rule Mining
  • Class 15, May 12: [PPT] Artificial Neural Networks
  • Class 16, May 19: [PPT] Genetic Algorithms

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.