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Cyprus International University
TR

ARTIFICIAL INTELLIGENCE

Course
CMPE415 - ARTIFICIAL INTELLIGENCE
Department
Computer Engineering - English - Undergraduate
Course Type
Course
Status
Required
Language
English
Credit
3
ECTS
7
T+P+L
3 + 0 + 1
Course Coordinator(s)
Assoc. Prof. Dr. Kamil YURTKAN
Prerequisite
Keywords

Course Description

This course teaches artificial intelligence from an intelligent systems perspective which includes the methods (tools) to build systems that can plan, learn, reason and interact intelligently with their environment. The course introduces the key components of the artificial intelligence (AI), the agent-based AI architecture, artificial intelligence techniques to solve problems for a particular domain, appropriate search methods in achieving desired goals, and knowledge representation using various techniques. The topics are as follows: intelligent agents, problem solving, uninformed search strategies, informed search strategies, knowledge representation, logical inference, propositional logic, first-order logic. The artificial intelligence methods studied are experimented using a programming language and the students are expected to complete a project related to an artificial intelligence algorithm with its software implementation.

ARTIFICIAL INTELLIGENCE

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

  1. 01 gain an understanding of the key components of the artificial intelligence (AI)
  2. 02 gain an understanding of the agent-based AI architecture
  3. 03 apply artificial intelligence techniques and classifiers in solving problems of a particular domain
  4. 04 implement basic learning algorithms in machine learning
  5. 05 represent knowledge using various techniques

Course Syllabus

Week Topic
Week 1 Introduction to AI and Problem Solving
Week 2 Intelligent Agents
Week 3 Intelligent Agents
Week 4 Introduction to Machine Learning
Week 5 Introduction to Neural Networks - Perceptron Learning
Week 6 Introduction to Neural Networks - Feed Forward Neural Networks
Week 7 Introduction to Neural Networks - Hopfield Network
Week 8 Uninformed Search Strategies - Breadth First Search, Uniform Cost Search, Depth First Search
Week 9 Uninformed Search Strategies - Breadth First Search, Uniform Cost Search, Depth First Search
Week 10 Midterm Week
Week 11 Midterm Week
Week 12 Informed Search Strategies - Greedy Search , A* Search
Week 13 Informed Search Strategies - Greedy Search , A* Search
Week 14 Representation and Logic - Propositional Logic
Week 15 Representation and Logic - First Order Logic

Reference Books & Course Materials

  1. 01 S. Russell, P. Norvig, "Artificial Intelligence: A Modern Approach", Prentice-Hall, 2003.
  2. 02 Simon Haykin, Neural Networks: A Comprehensive Foundation 2nd Ed. Prentice Hall PTR Upper Saddle River, NJ, USA ©1998 ISBN:0132733501

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

  1. P01 Should have sufficient knowledge in mathematics, science, and subjects specific to the relevant engineering discipline.
  2. P02 Should have the ability to use theoretical and applied knowledge in mathematics, science, and related engineering disciplines in complex engineering problems.
  3. P03 Should have the ability to detect, define, formulate, and solve complex engineering problems.
  4. P04 Should have the ability to select and apply appropriate analysis and modeling methods to solve complex engineering problems.
  5. P05 Should have the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions.
  6. P06 Should have the ability to apply modern design methods.
  7. P07 Should have the ability to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
  8. P08 Should have the ability to use information technologies effectively.
  9. P09 Should have the ability to design experiments, for the study of complex problems or discipline-specific research topics.
  10. P10 Should have the ability to conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research topics.
  11. P11 Should have the ability to work in intradisciplinary teams.
  12. P12 Should have the ability to work in interdisciplinary teams.
  13. P13 Should have the skills to work individually.
  14. P14 Should have the ability to communicate effectively verbally and in writing.
  15. P15 Should have the knowledge of at least one foreign language.
  16. P16 Should be able to write effective reports, understand written reports, and prepare design and production reports.
  17. P17 Should have the ability to make effective presentations.
  18. P18 Should have the ability to give and have clear and understandable instructions.
  19. P19 Should gain consciousness (awareness) about the necessity of lifelong learning.
  20. P20 Should have the ability to access information.
  21. P21 Should have the ability to follow developments in science and technology and constantly renew himself/herself.
  22. P22 Should gain the awareness of professional and ethical responsibility and should act in accordance with ethical principles.
  23. P23 Should gain knowledge about the standards used in engineering applications.
  24. P24 Should gain knowledge about project management, risk management, and change management practices in business life.
  25. P25 Should gain awareness about entrepreneurship, and innovation.
  26. P26 Should gain knowledge about development in sustainability.
  27. P27 Should gain knowledge about the effects of engineering practices on health, environment, and security at universal and social dimensions and the problems of the age reflected in the field of engineering.
  28. P28 Awareness should be gained about the legal consequences of engineering solutions.

Po-Lo Matrix

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