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Information
| Code | BMM027 |
| Name | Artificial Intelligence for Biomedical Engineering |
| Term | 2023-2024 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MUTLU AVCI |
| Course Instructor |
1 |
Course Goal / Objective
Comprehend artificial intelligence methods and gain knowledge on their biomedical applications.
Course Content
Fundamentals of regression and classification, learning algorithms, artificial neural networks, fuzzy logic, genetic algorithm, decision trees, support vector machines
Course Precondition
No prerequisite
Resources
Tom M. Mitchell, Machine Learning, McGraw Hill, 1997, ISBN: 9780070428072,0070428077.
Notes
E. Alpaydin, Introduction to Machine Learning, MIT Press, third edition, 2014, ISBN: 0262028182,9780262028189.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows classification and function approximation and their implementation differences. |
| LO02 | Knows the error minimization and required mathematical operations. |
| LO03 | Comprehends supervised learning MLP artificial neural networks. |
| LO04 | Comprehends supervised learning RBF artificial neural networks |
| LO05 | Recognizes supervised learning GRNN and PNN artificial neural networks. |
| LO06 | Knows unsupervised learning self orginizing map artificial neural network. |
| LO07 | Knows fuzzy logic and application areas. |
| LO08 | Knows genetical algorithm and uses it for optimization problems. |
| LO09 | Designs decision trees according to entropy and information gain. |
| LO10 | Knows Lagrangian interpolation and uses it for support vector machines. |
| LO11 | Uses support vector machine with kernel methods. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | To be able to solve scientific problems encountered in the field of medicine and medical technologies by applying current and advanced technical approaches of mathematics, science and engineering sciences. | 5 |
| PLO02 | Yetkinlikler - Öğrenme Yetkinliği | To have a knowledge of the literature related to a sub-discipline of biomedical engineering, to define and model current problems. | |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | Ability to analyze data, design and conduct experiments, and interpret results | 5 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Developing researched contemporary techniques and computational tools for engineering applications | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | To be able to analyze and design a process in line with a defined target | 4 |
| PLO06 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Conducting scientific studies with a medical doctor from an engineering perspective. | 3 |
| PLO07 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Expressing own findings orally and in writing, clearly and concisely. | |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | To be able to improve oneself by embracing the importance of lifelong learning and by following the developments in science-technology and contemporary issues. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to act independently, set priorities and creativity. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Being aware of national and international contemporary scientific and social problems in the field of Biomedical Engineering. | |
| PLO11 | Yetkinlikler - Alana Özgü Yetkinlik | To be able to evaluate the contribution of engineering solutions to problems in medicine, medical technologies and health in a global and social context. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Error minimization and LMS algorithm | Reading text book | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 2 | Gradient Descent, steepest descent and Levenberg Marquardt algorithms | Searching about the topic on intenet | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 3 | Introduction to artificial neural networks | Reading text book | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösteri |
| 4 | Supervised learning and Perceptron learning algoritm | Reading text book | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösteri |
| 5 | MLP artificial neural network | Reading text book | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | RBF artificial neural network | Reading text book | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösteri |
| 7 | GRNN and PNN artificial neural networks | Reading introduction papers | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Gösteri |
| 8 | Mid-Term Exam | Review of lecture notes and slides | Ölçme Yöntemleri: Ödev, Proje / Tasarım |
| 9 | Unsupervised learning and SOM artificial neural network | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Gösteri |
| 10 | Fuzzy Logic | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 11 | Decision tree | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 12 | Genetic algorithm | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 13 | Lagrangian interpolation | Searching about the topic on intenet | Öğretim Yöntemleri: Anlatım |
| 14 | Support vector machines | Reading lecture notes | Öğretim Yöntemleri: Anlatım, Gösteri |
| 15 | Working on datasets | Searching about the topic on intenet | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Review of lecture notes and slides | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Review | 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 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||