CE572 Artificial Intelligence Methods in EngineeringIstanbul Okan UniversityDegree Programs Geotechnics non-thesisGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Geotechnics non-thesis
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

General course introduction information

Course Code: CE572
Course Name: Artificial Intelligence Methods in Engineering
Course Semester: Fall
Course Credits:
Theoretical Practical Credit ECTS
3 0 3 10
Language of instruction: TR
Course Requisites:
Does the Course Require Work Experience?: No
Type of course: Department Elective
Course Level:
Master TR-NQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Assoc. Prof. SELİM DÜNDAR
Course Lecturer(s): Assoc. Prof. SELİM DÜNDAR
Course Assistants:

Course Objective and Content

Course Objectives: To experience application areas of modern artificial intelligence techniques.
Course Content: Introduction to artificial intelligence. History of artificial intelligence. Application areas of artificial intelligence. Various artificial intelligence techniques. Game theory. Fuzzy login. Artificial Neural Networks. Genetic Algorithms.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Recognizes artificial intelligence concept.
2 - Skills
Cognitive - Practical
1) Develops applications using artificial intelligence techniques.
3 - Competences
Communication and Social Competence
1) Researches and reports artificial intelligence applications
Learning Competence
Field Specific Competence
1) Applies artificial intelligence methods to engineering branch.
Competence to Work Independently and Take Responsibility

Lesson Plan

Week Subject Related Preparation
1) Introduction to Artificial Intelligence .
2) History of artificial intelligence .
3) Application areas of artificial intelligence .
4) Artificial intelligence movie
5) Problem solving
6) Artificial intelligence methods
7) Game theory
8) Fuzzy Logic I
9) Fuzzy Logic II
10) Artificial Neural Networks I
11) Aritificial Neural Networks II
12) Genetic Algorithms I
13) Genetic Algorithms II
14) Student Presentations

Sources

Course Notes / Textbooks: Ders Notları
References: Matlab paket programı

Course-Program Learning Outcome Relationship

Learning Outcomes

1

2

3

4

Program Outcomes
1) It defines the broad multidisciplinary scope of Geotechnical Engineering and the interaction between related disciplines.
2) Repeats current techniques and methods applied in the field of Geotechnical Engineering and their constraints, effects and results.
3) Systematically conveys the processes and results of studies in written, verbal and visual formats in national and international environments in the field of civil engineering or outside the field.

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) It defines the broad multidisciplinary scope of Geotechnical Engineering and the interaction between related disciplines.
2) Repeats current techniques and methods applied in the field of Geotechnical Engineering and their constraints, effects and results.
3) Systematically conveys the processes and results of studies in written, verbal and visual formats in national and international environments in the field of civil engineering or outside the field.

Learning Activity and Teaching Methods

Expression
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Group study and homework
Reading
Homework
Problem Solving
Project preparation
Report Writing
Q&A / Discussion
Application (Modelling, Design, Model, Simulation, Experiment etc.)
Web Based Learning

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Application
Individual Project
Presentation
Bilgisayar Destekli Sunum

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 1 % 20
Project 1 % 20
Final 1 % 60
total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
total % 100

Workload and ECTS Credit Grading

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 14 98
Presentations / Seminar 3 75
Project 3 75
Final 1 3
Total Workload 293