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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| BIOMEDICAL ENGINEERING (MASTER) (WITH THESIS) | |
| Code | BMM027 |
| Name | Artificial Intelligence for Biomedical Engineering |
| Term | 2018-2019 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 |
The current term course schedule has not been prepared yet.
|
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
Resources
Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Comprehend classification and function approximation |
| LO02 | Learning error minimization and required mathematical operations |
| LO03 | Comprehend supervised learning MLP artificial neural networks |
| LO04 | Comprehend supervised learning RBF artificial neural networks |
| LO05 | Recognize supervised learning GRNN and PNN artificial neural networks |
| LO06 | Understand unsupervised learning self orginizing map artificial neural network |
| LO07 | Learning fuzy logic |
| LO08 | To analyze genetical algorithm |
| LO09 | Design of decision trees |
| LO10 | Comprehend Lagrangian interpolation |
| LO11 | Recognize support vector machine |
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 | |
| 2 | Gradient Descent, steepest descent and Levenberg Marquardt algorithms | Searching about the topic on intenet | |
| 3 | Introduction to artificial neural networks | Reading text book | |
| 4 | Supervised learning and Perceptron learning algoritm | Reading text book | |
| 5 | MLP artificial neural network | Reading text book | |
| 6 | RBF artificial neural network | Reading text book | |
| 7 | GRNN and PNN artificial neural networks | Reading introduction papers | |
| 8 | Mid-Term Exam | Review of lecture notes and slides | |
| 9 | Unsupervised learning and SOM artificial neural network | Reading lecture notes | |
| 10 | Fuzzy Logic | Reading lecture notes | |
| 11 | Decision tree | Reading lecture notes | |
| 12 | Genetic algorithm | Reading lecture notes | |
| 13 | Lagrangian interpolation | Searching about the topic on intenet | |
| 14 | Support vector machines | Reading lecture notes | |
| 15 | Working on datasets | Searching about the topic on intenet | |
| 16 | Term Exams | Review of lecture notes and slides | |
| 17 | Term Exams |
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 | ||