PRINCIPLES of ARTIFICIAL INTELLIGENCE

AIM

The objective of this course is to introduce and teach the fundamentals of problems, theories, algorithms and applications of Artificial Intelligence (AI). AI is a very fast-growning field that focuses on building intelligent systems that will have a great impact on every aera of industry, economy, and social life. The topics include definition and history of AI, problem solving via search, game playing, knowledge representation, propositional logic, first-order predicate logic, logical and probabilistic reasoning, planning, uncertain knowledge and reasoning, machine learning (popular machine learning algorithms, deep learning, reinforcement learning, and genetic algorithms), natural language processing, deep learning for natural language processing, computer vision and robotics.

CONTENT

This course contains; Introduction and Intelligent Agents,Problem Solving by Searching,Adversarial Search and Games,Constraint Satisfaction Problems,Logical Agents,First-Order Logic, Inference in First-Order Logic,Knowledge Representation, Automated Planning,Uncertain knowledge and reasoning,Probabilistic Programming, Making Simple Decisions, Making Complex Decisions,Machine Learning,Deep Learning, Reinforcement Learning,Natural Language Processing, Deep Learning for Natural Language Processing,Computer Vision, Robotics,Review and presentations.

1. 1

   Students have an understanding of the key areas of artificial intelligence.

   Taught by: Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

2. 2

   Students cover a variety of search methods, knowledge representation, uncertainty, reasoning, machine learning, natural language processing, computer vision, and robotics.

   Taught by: Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

3. 3

   Students choose the appropriate algorithm to solve an AI problem.

   Taught by: Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

4. 4

   Students will be introduced to current research in the field of artificial intelligence and encouraged to identify research problems and develop effective solutions.

   Taught by: Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

5. 5

   Students cover a variety of search methods, knowledge representation, uncertainty, reasoning, machine learning, natural language processing, computer vision, and robotics.

   Taught by: Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

WEEKLY PLAN

1. WEEK 1

   Introduction and Intelligent Agents

2. WEEK 2

   Problem Solving by Searching

3. WEEK 3

   Adversarial Search and Games

4. WEEK 4

   Constraint Satisfaction Problems

5. WEEK 5

   Logical Agents

6. WEEK 6

   First-Order Logic, Inference in First-Order Logic

7. WEEK 7

   Knowledge Representation, Automated Planning

8. WEEK 8

   Uncertain knowledge and reasoning

9. WEEK 9

   Probabilistic Programming, Making Simple Decisions, Making Complex Decisions

10. WEEK 10

    Machine Learning

11. WEEK 11

    Deep Learning, Reinforcement Learning

12. WEEK 12

    Natural Language Processing, Deep Learning for Natural Language Processing

13. WEEK 13

    Computer Vision, Robotics

14. WEEK 14

    Review and presentations

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Artificial Intelligence: A Modern Approach, 4th Edition, by Stuart Russell and Peter Norvig, Pearson Education, 2021.
• Speech and Language Processing by Jurafsky and Martin, 2021. - G. F. Luger, Artificial Intelligence, Addison-Wesley, 2002. - Lectures notes ve web resources in AI.

TEACHING STAFF

• Prof.Dr. Selim AKYOKUŞCOORDINATOR
• Prof.Dr. Selim AKYOKUŞ