Code |
Name of the Course Unit |
Semester |
In-Class Hours (T+P) |
Credit |
ECTS Credit |
BIL422 |
ARTIFICIAL NEURAL NETWORKS |
5 |
3 |
3 |
6 |
GENERAL INFORMATION |
Language of Instruction : |
Turkish |
Level of the Course Unit : |
BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle |
Type of the Course : |
Elective |
Mode of Delivery of the Course Unit |
- |
Coordinator of the Course Unit |
Assist.Prof. SERKAN GÖNEN |
Instructor(s) of the Course Unit |
Lecturer KÜBRA ERDOĞAN |
Course Prerequisite |
No |
OBJECTIVES AND CONTENTS |
Objectives of the Course Unit: |
The aim of this course is to provide comprehensive knowledge on the design, training, and testing of artificial neural networks. |
Contents of the Course Unit: |
This course covers biological neurons and the brain, the model of a single neuron, neural networks, training algorithms, applications, benefits, weaknesses, and various uses of neural networks. |
KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to) |
Recognize and solve problems that can be addressed using artificial neural network algorithms. |
WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY |
Week |
Preparatory |
Topics(Subjects) |
Method |
1 |
- |
Overview of Artificial Intelligence and Machine Learning |
- |
2 |
- |
Introduction to Artificial Neural Networks |
- |
3 |
- |
Structure and Basic Elements of Artificial Neural Networks |
- |
4 |
- |
Early Artificial Neural Networks |
- |
5 |
- |
Artificial Neural Network Model (Supervised Learning) Multilayer Perceptron |
- |
6 |
- |
Artificial Neural Network Model (Supportive Learning) LVQ Model |
- |
7 |
- |
Artificial Neural Network Model (Unsupervised Learning) Adaptive Resonance Theory (ART) Networks |
- |
8 |
- |
MID-TERM EXAM |
- |
9 |
- |
Recurrent Networks (Element Networks) and Other Artificial Neural Network Models |
- |
10 |
- |
Hybrid Artificial Neural Networks |
- |
11 |
- |
Hardware for Artificial Neural Networks |
- |
12 |
- |
Overview of Applications of Artificial Neural Networks |
- |
13 |
- |
Student Project Presentations |
- |
14 |
- |
Student Project Presentations |
- |
15 |
- |
ANN: General Overview |
- |
16 |
- |
FINAL EXAM |
- |
17 |
- |
FINAL EXAM |
- |
SOURCE MATERIALS & RECOMMENDED READING |
Laurene V. Fausett, “Fundamentals of Neural Networks: Architectures, Algorithms And Applications”, Prentice Hall. |
Simon Haykin, “Neural Networks: A Comprehensive Foundation”, Prentice Hall |
Paul E. Keller, Kevin L. Priddy, "Artificial Neural Networks: an Introduction", PHI, 2007 |
Ercan Öztemel, “Yapay Sinir Ağları”, Papatya Yayıncılık, 2012 |
Vasif Nabiyev, "Yapay Zeka", Seçkin Yayınları, 3. baskı 2010 |
ASSESSMENT |
Assessment & Grading of In-Term Activities |
Number of Activities |
Degree of Contribution (%) |
Description |
Examination Method |
Mid-Term Exam |
1 |
30 |
|
Classical Exam |
Homework Assessment |
1 |
10 |
|
|
Short Exam |
1 |
10 |
|
|
Final Exam |
1 |
50 |
|
Classical Exam |
TOTAL |
4 |
100 |
|
|
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
CONTRIBUTION OF THE COURSE UNIT TO THE PROGRAMME LEARNING OUTCOMES
KNOWLEDGE |
Theoretical |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to identify, analyze, design, model and solve complex engineering problems based on engineering, science and mathematics fundamentals
|
|
|
|
|
|
5 |
KNOWLEDGE |
Factual |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to apply engineering design to produce solutions that meet specific needs, taking into account global, cultural, social, environmental and economic factors as well as public health, safety and well-being
|
|
|
|
|
4 |
|
SKILLS |
Cognitive |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to communicate effectively with various stakeholders
|
|
|
|
3 |
|
|
SKILLS |
Practical |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
The ability to recognize ethical and professional responsibilities in engineering and make informed decisions considering the impact of engineering solutions in their global, economic, environmental and social contexts
|
|
|
|
|
4 |
|
OCCUPATIONAL |
Autonomy & Responsibility |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
The ability to recognize ethical and professional responsibilities in engineering and make informed decisions considering the impact of engineering solutions in their global, economic, environmental and social contexts
|
|
|
|
|
4 |
|
OCCUPATIONAL |
Learning to Learn |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to acquire new knowledge and find ways to apply it when necessary, using appropriate learning strategies
|
|
|
|
3 |
|
|
OCCUPATIONAL |
Communication & Social |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to work effectively in a team where its members lead together, create a collaborative and inclusive environment, set goals, plan tasks, and meet goals
|
|
|
|
|
4 |
|
OCCUPATIONAL |
Occupational and/or Vocational |
|
Programme Learning Outcomes |
Level of Contribution |
0 |
1 |
2 |
3 |
4 |
5 |
1 |
Ability to design and conduct appropriate experiments, analyze and interpret data, and apply engineering principles to draw conclusions
|
|
|
|
3 |
|
|
WORKLOAD & ECTS CREDITS OF THE COURSE UNIT |
Workload for Learning & Teaching Activities |
Type of the Learning Activites |
Learning Activities (# of week) |
Duration (hours, h) |
Workload (h) |
Lecture & In-Class Activities |
14 |
3 |
42 |
Preliminary & Further Study |
14 |
3 |
42 |
Land Surveying |
0 |
0 |
0 |
Group Work |
0 |
0 |
0 |
Laboratory |
0 |
0 |
0 |
Reading |
0 |
0 |
0 |
Assignment (Homework) |
0 |
0 |
0 |
Project Work |
0 |
0 |
0 |
Seminar |
0 |
0 |
0 |
Internship |
0 |
0 |
0 |
Technical Visit |
0 |
0 |
0 |
Web Based Learning |
0 |
0 |
0 |
Implementation/Application/Practice |
0 |
0 |
0 |
Practice at a workplace |
0 |
0 |
0 |
Occupational Activity |
0 |
0 |
0 |
Social Activity |
0 |
0 |
0 |
Thesis Work |
0 |
0 |
0 |
Field Study |
0 |
0 |
0 |
Report Writing |
0 |
0 |
0 |
Final Exam |
1 |
2 |
2 |
Preparation for the Final Exam |
1 |
35 |
35 |
Mid-Term Exam |
1 |
2 |
2 |
Preparation for the Mid-Term Exam |
1 |
30 |
30 |
Short Exam |
0 |
0 |
0 |
Preparation for the Short Exam |
0 |
0 |
0 |
TOTAL |
32 |
0 |
153 |
|
Total Workload of the Course Unit |
153 |
|
|
Workload (h) / 25.5 |
6 |
|
|
ECTS Credits allocated for the Course Unit |
6,0 |
|