Course
AIE1116637
INTRODUCTION to ARTIFICIAL INTELLIGENCE ENGINEERING
Artificial Intelligence Engineering
- LECTURE
- 2
- LAB
- 2
- CREDITS
- 3
- ECTS
- 4
AIM
The aim of this course is to explain artificial intelligence engineering and describe its main fields of study.
CONTENT
This course contains; Introduction to Engineering Profession and Career,Introduction to Engineering Design,Circuits,Circuits,Signals and Systems,Signals and Systems,Probability and Statistics in Engineering,Exam Week,Probability and Statistics in Engineering,An introduction to Computer Science,Data Science,Introduction to Algorithms,Machine Learning and Artificial Intelligence,Software Engineering, UML, and State Machines.
LEARNING OUTCOMES
- 1
1. Define Artificial intelligence engineering
Taught by: Self Study Method, Question - Answer Technique, Brainstorming Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework
- 2
2. Explain different fields of Artificial intelligence engineering
Taught by: Self Study Method, Brainstorming Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework
- 3
3. Summarize social, professional, and ethical issues
Taught by: Discussion Method, Self Study Method, Brainstorming Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework
- 4
4. Translate innovation and entrepreneurship issues
Taught by: Discussion Method, Self Study Method, Brainstorming Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework
- 5
5. Understand the steps required to design complex systems.
Taught by: Experimental Technique, Simulation Technique, Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task
WEEKLY PLAN
- WEEK 1
Introduction to Engineering Profession and Career
Preparation: Lecture Slides Week 1
- WEEK 2
Introduction to Engineering Design
Preparation: Lecture Slides Week 2
- WEEK 3
Circuits
Preparation: Lecture Slides Week 3
- WEEK 4
Circuits
Preparation: Lecture Slides Week 3
- WEEK 5
Signals and Systems
Preparation: Lecture Slides Week 5
- WEEK 6
Signals and Systems
Preparation: Lecture Slides Week 5
- WEEK 7
Probability and Statistics in Engineering
Preparation: Lecture Slides Week 7
- WEEK 8
Exam Week
Preparation: All lecture slides till Week 7
- WEEK 9
Probability and Statistics in Engineering
Preparation: Lecture Slides Week 9
- WEEK 10
An introduction to Computer Science
Preparation: Lecture Slides Week 10
- WEEK 11
Data Science
Preparation: Lecture Slides Week 11
- WEEK 12
Introduction to Algorithms
Preparation: Lecture Slides Week 12
- WEEK 13
Machine Learning and Artificial Intelligence
Preparation: Lecture Slides Week 13
- WEEK 14
Software Engineering, UML, and State Machines
Preparation: Lecture Slides Week 14
ASSESSMENT
- Rate of Midterm Exam to Success30%
- Rate of Final Exam to Success70%
WORKLOAD
| ACTIVITY | COUNT | HOURS | TOTAL |
|---|---|---|---|
| Course Hours | 13 | 2 | 26 |
| Guided Problem Solving | 0 | 0 | 0 |
| Resolution of Homework Problems and Submission as a Report | 10 | 4 | 40 |
| Term Project | 1 | 6 | 6 |
| Presentation of Project / Seminar | 1 | 24 | 24 |
| Quiz | 0 | 0 | 0 |
| Midterm Exam | 1 | 12 | 12 |
| General Exam | 1 | 12 | 12 |
| Performance Task, Maintenance Plan | 0 | 0 | 0 |
READING
- Powerpoint slides
- 1. Saeed Moaveni, “Engineering Fundamentals: An Introduction to Engineering” Cengage Learning, 5th edition. 2. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-01sc-introduction-to-electrical-engineering-and-computer-science-i-spring-2011/Syllabus/MIT6_01SCS11_notes.pdf
TEACHING STAFF
- Prof.Dr. Mehmet Kemal ÖZDEMİRCOORDINATOR
- Prof.Dr. Selim AKYOKUŞ
- Prof.Dr. Cem ÜNSALAN
- Assist.Prof. Ahmet KAPLAN
- Prof.Dr. Reda ALHAJJ
- Assist.Prof. Mustafa TÜRKBOYLARI
- Prof.Dr. Mehmet Kemal ÖZDEMİR
- Assist.Prof. İbrahim KARLIAĞA
- Assist.Prof. Mustafa AKTAN
- Assist.Prof. Tankut AKGÜL