TÜRKÇE
ENGLISH
Information
| Code | BMS429 |
| Name | Artificial Intelligence Systems |
| Term | 2024-2025 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MUTLU AVCI |
| Course Instructor |
Prof. Dr. MUTLU AVCI
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
Learning the basic artificial intelligence techniques and understanding implementation of artificial intelligence on engineering problems.
Course Content
Fundamentals of artificial intelligence, regression techniques, classification techniques, learning algorithms, artificial neural networks, genetc algorithm, decision trees, fuzzy logic, support vector machines
Course Precondition
No prerequisite
Resources
Lecture Notes and slides are available.
Notes
Vasif V. Nabiyev, Artificial Intelligence, Seckin Publication, 2005.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Recognize smart and intelligent systems. |
| LO02 | Explain the learning algorithms. |
| LO03 | Know the regression and classification concepts. |
| LO04 | Capable of training artificial neural networks. |
| LO05 | Explain the genetic algorithm. |
| LO06 | Capable of implementing decision trees. |
| LO07 | Knows and uses fuzzy logic. |
| LO08 | Know support vector machines. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have sufficient knowledge in mathematics, natural sciences, and biomedical engineering, along with the ability to use theoretical and applied knowledge in these areas to solve complex engineering problems. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquire the ability to identify, formulate, and solve complex Biomedical Engineering problems; for this purpose, will have the ability to choose and apply appropriate analysis and modeling methods. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | The ability to design a complex system, process, device, or product in Biomedical Engineering under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | The ability to select and use modern techniques and tools necessary for analyzing and solving complex problems encountered in Biomedical Engineering applications; the ability to use information technologies effectively. | 5 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics in Biomedical Engineering. | |
| PLO06 | Bilgi - Kuramsal, Olgusal | The ability to work effectively in intra-disciplinary (Biomedical Engineering) and multi-disciplinary teams; ability to work individually. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | The ability to communicate effectively verbally and in writing; knowledge of at least one foreign language; ability to write effective reports in Biomedical Engineering and understand written reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. | 4 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Get awareness of the necessity of lifelong learning; the ability to access information in the field of Biomedical Engineering, to follow developments in science and technology, and the ability to constantly self-renewal. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Acting following ethical principles, professional and ethical responsibility in the field of Biomedical Engineering, and knowledge of the standards used in engineering practice. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Knowledge of project management and practices in the field of Biomedical Engineering, such as risk management and change management; awareness about entrepreneurship, innovation, and sustainable development. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Understanding the universal and societal impacts of Biomedical Engineering applications on health, environment, and safety; awareness of the legal implications of Biomedical Engineering solutions. | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Understanding of biology and physiology. | |
| PLO13 | Beceriler - Bilişsel, Uygulamalı | Ability to make measurements on living systems and interpret data collected from these measurements. | |
| PLO14 | Bilgi - Kuramsal, Olgusal | Ability to solve problems related to the interactions between living and nonliving materials and systems. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to artificial intelligence | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 2 | Error minimization and regression | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 3 | Artificial neural networks and learning algorithms | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 4 | Error backpropagation learning | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 5 | Multi Layer Perceptron ANN | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 6 | Radial Basis Function ANN | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 7 | General regression neural network | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Reading lecture materials | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Probabilistic neural network | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 10 | Genetic algorithm | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 11 | Decision trees | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 12 | Fuzzy logic | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 13 | Support vector machines 1 | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 14 | Support vector machines 2 | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 15 | Self Orginizing Map | Reading lecture materials | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Test and classical mixed exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Test and classical mixed exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 18 | 18 |
| Total Workload (Hour) | 128 | ||
| Total Workload / 25 (h) | 5,12 | ||
| ECTS | 5 ECTS | ||