CS362 Introduction to Artificial Intelligence

CS362: Introduction to Artificial Intelligence

Classes: Tue 09.30 – 12.30 am

Location: 

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

Objectives:

  • 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.

Texts:

  • 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

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.

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