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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 Be able to understand and apply security protocol and tools to security challenges faced in organizations
  2. P02 Be able to design security software to combat security issues
  3. P03 Be able to identify, categorize, and develop security solutions for computer orientated challenges.
  4. P04 Be able to demonstrate autonomy and responsibility in managing computer security projects
  5. P05 Be able to follow the state of the arts concepts in computer technology security
  6. P06 Be able to design, implement, and evaluate a computational system to meet desired security needs within realistic constraints
  7. P07 Be able to use appropriate security techniques, protocols, skills, and tools necessary for securing computer systems
  8. P08 Be able to apply effective communication skills consistent with the professional environment
  9. P09 Be able to apply effective collaboration skills in teamwork consistent with the professional environment
  10. P10 Be able to apply appropriate security technology and techniques to facilitate a safe operation in an organization

Po-Lo Matrix

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