MACHINE LEARNING APPLICATION in HEALTHCARE

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.

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.

1. 1

   The Ability to implement a basic machine learning algorithm

   Taught by: Self Study Method, Lecture Method · Assessed by: Project Task, Performance Task

2. 2

   The Ability to recognize different neural network structures

   Taught by: Self Study Method, Lecture Method · Assessed by: Project Task, Performance Task

3. 3

   The ability to decide which type of machine learning is appropriate for specific applications.

   Taught by: Self Study Method, Lecture Method · Assessed by: Project Task, Performance Task

4. 4

   Gain familiarity with the state of the art of machine learning applications in different areas of healthcare

   Taught by: Self Study Method, Lecture Method · Assessed by: Project Task, Performance Task

WEEKLY PLAN

1. WEEK 1

   Introduction to Machine Learning

   Preparation: Week 1 presentation notes.

2. WEEK 2

   Machine Learning Implementation Steps-I

   Preparation: Week 2 presentation notes.

3. WEEK 3

   Machine Learning Implementation Steps-II

   Preparation: Week 3 presentation notes.

4. WEEK 4

   Types of Machine Learning

   Preparation: Week 4 presentation notes.

5. WEEK 5

   Performance Parameters in Machine Learning

   Preparation: Week 5 presentation notes.

6. WEEK 6

   Deep Learning-I

   Preparation: Presentation notes: Personalized Medicine Applications

7. WEEK 7

   Deep Learning-II

   Preparation: Presentation notes: Personalized Medicine Applications

8. WEEK 8

   Regresssion Algorithms-I

   Preparation: Presentation notes: Personalized Medicine Applications

9. WEEK 9

   Regresssion Algorithms-II

   Preparation: Presentation notes: Personalized Medicine Applications

10. WEEK 10

    Classification

    Preparation: Presentation notes: Applications for Specific Diseases

11. WEEK 11

    Machine Learning Applications in Healthcare-I

    Preparation: Presentation notes: Applications for Specific Diseases

12. WEEK 12

    Machine Learning Applications in Healthcare-II

    Preparation: Presentation notes: Applications for Specific Diseases

13. WEEK 13

    Machine Learning Applications in Healthcare-III

    Preparation: Presentation notes: Applications for Specific Diseases

14. WEEK 14

    Machine Learning Applications in Healthcare-IV

    Preparation: Presentation notes: Applications for Specific Diseases

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• S. N. Mohanty, G. Nalinipiriya, Machine Learning for Healthcare Applications, First Edition, 13 April 2021, Wiley-Scrivener Publishing, ISBN: 978-1119791812

TEACHING STAFF

• Assoc.Prof. Yasin GÖÇGÜNCOORDINATOR
• Assoc.Prof. Yasin GÖÇGÜN