BMM027 Artificial Intelligence for Biomedical Engineering

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Code BMM027
Name Artificial Intelligence for Biomedical Engineering
Semester . Semester
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 Goal

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