ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENGINEERING
1
Course Title:
ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENGINEERING
2
Course Code:
OTO4049
3
Type of Course:
Optional
4
Level of Course:
First Cycle
5
Year of Study:
4
6
Semester:
7
7
ECTS Credits Allocated:
4
8
Theoretical (hour/week):
3
9
Practice (hour/week) :
0
10
Laboratory (hour/week) :
0
11
Prerequisites:
None
12
Recommended optional programme components:
None
13
Language:
Turkish
14
Mode of Delivery:
Face to face
15
Course Coordinator:
Doç. Dr. EMRE İSA ALBAK
16
Course Lecturers:
Yok
17
Contactinformation of the Course Coordinator:
emrealbak@uludag.edu.tr
18
Website:
None
19
Objective of the Course:
The objective of this course is to introduce engineering students to the fundamental principles and application areas of artificial intelligence.
20
Contribution of the Course to Professional Development
This course enhances engineering students' knowledge and skills in artificial intelligence, enabling them to create innovative solutions for modern engineering challenges.
21
Learning Outcomes:
1
Define AI technologies and fundamental principles in the context of engineering.;
2
Apply machine learning and deep learning algorithms for data analysis.;
3
Select appropriate models and algorithms for engineering problems related to AI.;
4
Develop solutions that enhance efficiency through optimization using AI algorithms.;
5
Evaluate ethics, security, and privacy concerns in AI applications.;
6
Analyze potential applications of AI across various engineering fields to develop solution-oriented projects.;
22
Course Content:
Week
Theoretical
Practical
1
Introduction and AI Concepts
2
Basics for AI
3
Machine Learning Methods and Classification
4
Data Processing and Feature Extraction
5
K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
6
Decision Trees and Ensemble Learning Methods
7
Artificial Neural Networks (ANN)
8
Deep Learning and Convolutional Neural Networks (CNN)
9
Natural Language Processing (NLP) and Recurrent Neural Networks (RNN)
10
Reinforcement Learning
11
Programming Languages and Libraries for AI Applications
12
Optimization Techniques with Artificial Intelligence
13
Project Presentations and Course Evaluation
14
Project Presentations and Course Evaluation
23
Textbooks, References and/or Other Materials:
24
Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
1
20
Quiz
0
0
Homeworks, Performances
1
20
Final Exam
1
60
Total
3
100
Contribution of Term (Year) Learning Activities to Success Grade
40
Contribution of Final Exam to Success Grade
60
Total
100
Measurement and Evaluation Techniques Used in the Course
Midterm exam, Final exam, Homework
Information
The results are determined by the letter grade determined by the student automation system.
25
ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
3
42
Practicals/Labs
0
0
0
Self Study and Preparation
14
2
28
Homeworks, Performances
1
15
15
Projects
0
0
0
Field Studies
0
0
0
Midtermexams
1
15
15
Others
0
0
0
Final Exams
1
20
20
Total WorkLoad
135
Total workload/ 30 hr
4
ECTS Credit of the Course
4
26
CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
