GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
ARTIFICIAL INTELLIGENCE APPLICATIONS IN MEDICINE/5121309
Course Title: ARTIFICIAL INTELLIGENCE APPLICATIONS IN MEDICINE
Credits 3 ECTS 8
Semester 2 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Dr.Oktay YILDIZ
 -- WEB SITE(S) OF LECTURER(S)
  http://w3.gazi.edu.tr/~oyildiz/
 -- EMAIL(S) OF LECTURER(S)
  oyildiz@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
To learn computer-aided analysis and classification of medical data and images and, basic artificial intelligence techniques and algorithms apply them








 -- MODE OF DELIVERY
  distance learning
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  Overview of AI in Medicine
2. Week  Machine Learning and Concept Learning
3. Week  Probabilistic and Bayesian learning
4. Week  Classification techniques
5. Week  K nearest neighbor, Decision trees
6. Week  Support Vector Machines
7. Week  Evaluation of learning algorithms
8. Week  Midterm
9. Week  Artificial Neural Networks
10. Week  Genetic Algorithms
11. Week  Medical Imaging Modalities
12. Week  Medical Image Processing and Enhancement
13. Week  Medical Image Analysis, and Classification
14. Week  Project Presentations
15. Week  Project Presentations
16. Week  Project Presentations
 -- RECOMMENDED OR REQUIRED READING
  Introduction To Machine Learning And Bioinformatics / Sushmita Mitra Medical Image Analysis / Atam P. Dhawan
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  Not Applicable
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
0
0
 Assignment
2
10
 Exercises
1
15
 Projects
1
15
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
15
1
15
 Practising Hours of Course Per Week
0
 Reading
15
3
45
 Searching in Internet and Library
15
3
45
 Designing and Applying Materials
0
 Preparing Reports
5
3
15
 Preparing Presentation
5
3
15
 Presentation
1
1
1
 Mid-Term and Studying for Mid-Term
6
3
18
 Final and Studying for Final
6
3
18
 Other
6
3
18
 TOTAL WORKLOAD: 
190
 TOTAL WORKLOAD / 25: 
7.6
 ECTS: 
8
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1-Improve and deepen their knowledge in the field at expert level based on Bachelor’s degree competenciesX
2-Understand the interaction between the disciplines related to the fieldX
3-Use the theoretical and applied knowledge that he/she acquires in thefieldX
4-Develop new knowledge combining the knowledge from the related disciplines and the knowledge in the fieldX
5-Analyze the problems in the field using scientific research methodsX
6- Identify a problem in the fieldX
7- Develop suggestions for the problems in the fieldX
8-Solves the problemX
9Evaluate solution results in terms of quality processesX
10-Develop new approaches and methods by taking responsibility in complex situations encountered in practice of the fieldX