The aim of this course is to teach students the fundamental concepts, application steps, and different types of machine learning; to introduce regression and classification algorithms as well as deep learning approaches from both theoretical and practical perspectives; and to demonstrate how machine learning methods are used in the healthcare sector. Within the scope of the course, students are expected to develop data-driven problem-solving skills, gain the ability to select appropriate algorithms, and evaluate the performance of machine learning models.
Course Content
This course contains; Introduction to Machine Learning ,Machine Learning Implementation Steps-I,Machine Learning Implementation Steps-II,Types of Machine Learning,Performance Parameters in Machine Learning,Deep Learning-I,Deep Learning-II,Regresssion Algorithms-I,Regresssion Algorithms-II,Classification,Machine Learning Applications in Healthcare-I,Machine Learning Applications in Healthcare-II,Machine Learning Applications in Healthcare-III,Machine Learning Applications in Healthcare-IV.
Course Learning Outcomes
Teaching Methods
Assessment Methods
The Ability to implement a basic machine learning algorithm
14, 9
F, H
The Ability to recognize different neural network structures
14, 9
F, H
The ability to decide which type of machine learning is appropriate 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 Implementation Steps-I
Week 2 presentation notes.
3
Machine Learning Implementation Steps-II
Week 3 presentation notes.
4
Types of Machine Learning
Week 4 presentation notes.
5
Performance Parameters in Machine Learning
Week 5 presentation notes.
6
Deep Learning-I
Presentation notes: Personalized Medicine Applications
7
Deep Learning-II
Presentation notes: Personalized Medicine Applications
8
Regresssion Algorithms-I
Presentation notes: Personalized Medicine Applications
9
Regresssion Algorithms-II
Presentation notes: Personalized Medicine Applications
10
Classification
Presentation notes: Applications for Specific Diseases
11
Machine Learning Applications in Healthcare-I
Presentation notes: Applications for Specific Diseases
12
Machine Learning Applications in Healthcare-II
Presentation notes: Applications for Specific Diseases
13
Machine Learning Applications in Healthcare-III
Presentation notes: Applications for Specific Diseases
14
Machine Learning Applications in Healthcare-IV
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
Assoc.Prof. Yasin GÖÇGÜN
Name of Lecturer(s)
Assoc.Prof. Yasin GÖÇGÜN
Assistant(s)
Aim
The aim of this course is to teach students the fundamental concepts, application steps, and different types of machine learning; to introduce regression and classification algorithms as well as deep learning approaches from both theoretical and practical perspectives; and to demonstrate how machine learning methods are used in the healthcare sector. Within the scope of the course, students are expected to develop data-driven problem-solving skills, gain the ability to select appropriate algorithms, and evaluate the performance of machine learning models.
Course Content
This course contains; Introduction to Machine Learning ,Machine Learning Implementation Steps-I,Machine Learning Implementation Steps-II,Types of Machine Learning,Performance Parameters in Machine Learning,Deep Learning-I,Deep Learning-II,Regresssion Algorithms-I,Regresssion Algorithms-II,Classification,Machine Learning Applications in Healthcare-I,Machine Learning Applications in Healthcare-II,Machine Learning Applications in Healthcare-III,Machine Learning Applications in Healthcare-IV.
Course Learning Outcomes
Teaching Methods
Assessment Methods
The Ability to implement a basic machine learning algorithm
14, 9
F, H
The Ability to recognize different neural network structures
14, 9
F, H
The ability to decide which type of machine learning is appropriate 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 Implementation Steps-I
Week 2 presentation notes.
3
Machine Learning Implementation Steps-II
Week 3 presentation notes.
4
Types of Machine Learning
Week 4 presentation notes.
5
Performance Parameters in Machine Learning
Week 5 presentation notes.
6
Deep Learning-I
Presentation notes: Personalized Medicine Applications
7
Deep Learning-II
Presentation notes: Personalized Medicine Applications
8
Regresssion Algorithms-I
Presentation notes: Personalized Medicine Applications
9
Regresssion Algorithms-II
Presentation notes: Personalized Medicine Applications
10
Classification
Presentation notes: Applications for Specific Diseases
11
Machine Learning Applications in Healthcare-I
Presentation notes: Applications for Specific Diseases
12
Machine Learning Applications in Healthcare-II
Presentation notes: Applications for Specific Diseases
13
Machine Learning Applications in Healthcare-III
Presentation notes: Applications for Specific Diseases
14
Machine Learning Applications in Healthcare-IV
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.
