To briefly introduce machine learning and deep learning and to examine the current applications of these techniques in the healthcare field today.
Course Content
This course contains; Introduction to Machine Learning
,Machine Learning,Deep Learning Review,Diagnostic Applications,Electronic Health Records Applications,Personalized Medicine Applications,Personalized Medicine Applications-2,Personalized Medicine Applications-3,Personalized Medicine Applications-4,Applications for Specific Diseases,Applications for Specific Diseases-2,Applications for Specific Diseases-3,Applications for Specific Diseases-4,Applications for Specific Diseases-5.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Ability to implement a basic machine learning algorithm
14, 9
F, H
Ability to recognize different neural network structures
14, 9
F, H
Ability to decide which type of network structure is suitable for specific applications
14, 9
F, H
Gain familiarity with the state of the art of machine learning applications in different areas of healthcare
14, 9
F, H
Teaching Methods:
14: Self Study Method, 9: Lecture Method
Assessment Methods:
F: Project Task, H: Performance Task
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Machine Learning
Week 1 presentation notes.
2
Machine Learning
Week 2 presentation notes.
3
Deep Learning Review
Week 3 presentation notes.
4
Diagnostic Applications
Week 4 presentation notes.
5
Electronic Health Records Applications
Week 5 presentation notes.
6
Personalized Medicine Applications
Presentation notes: Personalized Medicine Applications
7
Personalized Medicine Applications-2
Presentation notes: Personalized Medicine Applications
8
Personalized Medicine Applications-3
Presentation notes: Personalized Medicine Applications
9
Personalized Medicine Applications-4
Presentation notes: Personalized Medicine Applications
10
Applications for Specific Diseases
Presentation notes: Applications for Specific Diseases
11
Applications for Specific Diseases-2
Presentation notes: Applications for Specific Diseases
12
Applications for Specific Diseases-3
Presentation notes: Applications for Specific Diseases
13
Applications for Specific Diseases-4
Presentation notes: Applications for Specific Diseases
14
Applications for Specific Diseases-5
Presentation notes: Applications for Specific Diseases
Resources
S. N. Mohanty, G. Nalinipiriya, Machine Learning for Healthcare Applications, First Edition, 13 April 2021, Wiley-Scrivener Publishing, ISBN: 978-1119791812
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with.
X
3
Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods.
X
4
Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge.
X
5
Independently conduct studies that require proficiency in the field.
6
Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field.
7
Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning.
X
8
Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level.
X
9
Define the social and environmental aspects of engineering applications.
10
Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values.
X
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
50
Rate of Final Exam to Success
50
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
3
1
3
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
137
1
137
Term Project
0
0
0
Presentation of Project / Seminar
100
1
100
Quiz
0
0
0
Midterm Exam
0
0
0
General Exam
0
0
0
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
240
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30)
8
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
MACHINE LEARNING APPLICATION in HEALTHCARE
SSMY1212823
Spring Semester
3+0
3
8
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
Turkish
Course Level
Second Cycle (Master's Degree)
Course Type
Elective
Course Coordinator
Assist.Prof. Merve Yüsra DOĞAN
Name of Lecturer(s)
Assist.Prof. Merve Yüsra DOĞAN
Assistant(s)
Aim
To briefly introduce machine learning and deep learning and to examine the current applications of these techniques in the healthcare field today.
Course Content
This course contains; Introduction to Machine Learning
,Machine Learning,Deep Learning Review,Diagnostic Applications,Electronic Health Records Applications,Personalized Medicine Applications,Personalized Medicine Applications-2,Personalized Medicine Applications-3,Personalized Medicine Applications-4,Applications for Specific Diseases,Applications for Specific Diseases-2,Applications for Specific Diseases-3,Applications for Specific Diseases-4,Applications for Specific Diseases-5.
Dersin Öğrenme Kazanımları
Teaching Methods
Assessment Methods
Ability to implement a basic machine learning algorithm
14, 9
F, H
Ability to recognize different neural network structures
14, 9
F, H
Ability to decide which type of network structure is suitable for specific applications
14, 9
F, H
Gain familiarity with the state of the art of machine learning applications in different areas of healthcare
14, 9
F, H
Teaching Methods:
14: Self Study Method, 9: Lecture Method
Assessment Methods:
F: Project Task, H: Performance Task
Course Outline
Order
Subjects
Preliminary Work
1
Introduction to Machine Learning
Week 1 presentation notes.
2
Machine Learning
Week 2 presentation notes.
3
Deep Learning Review
Week 3 presentation notes.
4
Diagnostic Applications
Week 4 presentation notes.
5
Electronic Health Records Applications
Week 5 presentation notes.
6
Personalized Medicine Applications
Presentation notes: Personalized Medicine Applications
7
Personalized Medicine Applications-2
Presentation notes: Personalized Medicine Applications
8
Personalized Medicine Applications-3
Presentation notes: Personalized Medicine Applications
9
Personalized Medicine Applications-4
Presentation notes: Personalized Medicine Applications
10
Applications for Specific Diseases
Presentation notes: Applications for Specific Diseases
11
Applications for Specific Diseases-2
Presentation notes: Applications for Specific Diseases
12
Applications for Specific Diseases-3
Presentation notes: Applications for Specific Diseases
13
Applications for Specific Diseases-4
Presentation notes: Applications for Specific Diseases
14
Applications for Specific Diseases-5
Presentation notes: Applications for Specific Diseases
Resources
S. N. Mohanty, G. Nalinipiriya, Machine Learning for Healthcare Applications, First Edition, 13 April 2021, Wiley-Scrivener Publishing, ISBN: 978-1119791812
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with.
X
3
Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods.
X
4
Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge.
X
5
Independently conduct studies that require proficiency in the field.
6
Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field.
7
Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning.
X
8
Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level.
X
9
Define the social and environmental aspects of engineering applications.
10
Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values.
