• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS YBS 6016 EXPERT SYSTEMS ELECTIVE 3 0 0 7

Offered By

Management Information Systems

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR VAHAP TECIM

Offered to

Management Information Systems

Course Objective

The objective of this course is to give the theoretical background to the graduate students who conduct research in fields which use artificial intelligence, such as decision making, machine learning, computer vision.

Learning Outcomes of the Course Unit

1   Define basic artificial intelligence concepts 2   Apply artificial intelligence techniques 3   Determine which Artificial Intelligence technique is appropriate to solve a particular problem 4   Design an artificial intelligent model 5   Implement an AI algorithm

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Artificial Intelligence 2 Real World AI Applications 3 Pattern Discovery with AI techniques 4 Problem Solving Techniques 5 Association Rules and Decision Making 6 Artificial Neural Networks 7 Self-Organizing Map (SOM) 8 Case Studies 9 Pattern Recognition 10 Knowledge Representation 11 Artificial Life 12 Expert Systems 13 AI Robotics 14 Presentations

Recomended or Required Reading

To be announced.

Planned Learning Activities and Teaching Methods

The activities are detailed in the 'Assessment Methods' and 'Workload Calculation' sections.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 PRJ PROJECT 3 PRS PRESENTATION 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTE* 0.20 + PRJ* 0.20 + PRS* 0.20 + FIN* 0.40 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + PRJ * 0.20 + PRS * 0.20 + RST* 0.40

Further Notes About Assessment Methods

None

Assessment Criteria

Students' performances are measured with a midterm exam, a project, a presentation and a final exam.

Language of Instruction

Turkish

Course Policies and Rules

The rules applied by the department apply.

Contact Details for the Lecturer(s)

Prof.Dr. Vahap TECIM vahap.tecim@deu.edu.tr

Office Hours

It will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparations before/after weekly lectures 13 4 52 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Preparing presentations 1 10 10 Preparing report 1 27 27 Final 1 1 1 Midterm 1 1 1 TOTAL WORKLOAD (hours) 170

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 LO.1 5 3 3 4 LO.2 5 3 3 4 LO.3 5 3 3 4 LO.4 5 3 3 4 LO.5 5 3 3 4