Midwifery Preview
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code:

UNI220

Course Name:

Machine Learning and Data Science

Semester:

Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

University Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

E-Learning

Course Coordinator:

Dr. Öğr. Üy. ALPER ÖNER

Course Lecturer(s):

Ferzat Anka

Course Assistants:

Course Objective and Content

Course Objectives:	The aim of the course is to provide students with information on basic techniques and methods in artificial learning and to enable students to have the ability to use artificial learning methods in solving practical problems. At the same time, it is to understand the importance of machine learning in today's application areas.
Course Content:	Machine learning basic concepts and methods. Problem solving using machine learning; methods using and not using problem information. Data analysis, To examine various algorithms. To explain the importance of artificial intelligence methods in different fields with examples

Learning Outcomes

The students who have succeeded in this course;
1) • Recognize the problems that can be solved by machine learning methods.
2) • Understanding the importance of artificial intelligence in solving various problems
3) • Can choose the appropriate machine learning method for the given problem.
4) • Can solve the given problem with the appropriate machine learning method.
5) • Knows the ways of representing information, its advantages and disadvantages.

Course Flow Plan

Week	Subject	Related Preparation
1)	Machine learning history and philosophy
2)	Basic concepts
3)	Basic concepts-Intelligent Agents
4)	Introduction to machine learning and problem solving and search algorithms
5)	Expert systems and machine learning
6)	Optimization methods in machine learning
7)	Homework-Presentation
8)	Homework-Presentation
9)	Homework-Presentation
10)	Data science and analysis
11)	Machine learning
12)	Data science and methods
13)	Machine learning
14)	Search algorithms and their importance (Definite, greedy, heuristic, meta-heuristic)

Sources

Course Notes / Textbooks:

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010,
• Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition
• Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi 4. Baskı
• Yalçin Özkan, Veri Madenciliği Yöntemleri, Papatya, 2008
• Cemalettin Kubat, Matlab Yapay Zeka ve Mühendislik uygulamaları, Pusula, 2009
• İlker Arslan, R ile İstatistiksel Programlama, Pusula, 2020
• Zafer Demirkol, Herkes İçin Yapay Zeka, Genç Destek, 2021
• S.Nematzadeh et al. Rationalized Statistics for Biosciences Analysing bioinformatics data using the R, LAP Publishing, 2021

References:

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Using the concepts and theories that constitute the basis of care, they know and plan appropriate initiatives in health protection and development.
2) Uses the basic scientific knowledge about health, social and behavioral sciences in midwifery practices through information and communication technologies.
3) In the period between the formation of the pregnancy and the birth, it can give the qualified care which is in need of normal / risk pregnant and his / her family.
4) They can meet the care needs of the pregnant woman and her family during labor and can carry out qualified care and appropriate procedures specific to normal birth stages.
5) Can meet the care needs of normal / risky woman, newborn and family at the postpartum period and make appropriate attempts.
6) To be able to define common gynecological problems in women and to plan and implement necessary care and interventions and to do health education on this subject.
7) It can provide health education and counseling services for women's reproductive health, women of all walks of life and all ages.
8) Performs midwifery practices in line with evidence-based care principles.
9) It establishes positive communication / cooperation with the team in health related practices and can work as an effective team member.
10) It acts in accordance with the ethical principles and values of midwifery profession.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Using the concepts and theories that constitute the basis of care, they know and plan appropriate initiatives in health protection and development.	3
2)	Uses the basic scientific knowledge about health, social and behavioral sciences in midwifery practices through information and communication technologies.	3
3)	In the period between the formation of the pregnancy and the birth, it can give the qualified care which is in need of normal / risk pregnant and his / her family.	3
4)	They can meet the care needs of the pregnant woman and her family during labor and can carry out qualified care and appropriate procedures specific to normal birth stages.	3
5)	Can meet the care needs of normal / risky woman, newborn and family at the postpartum period and make appropriate attempts.	3
6)	To be able to define common gynecological problems in women and to plan and implement necessary care and interventions and to do health education on this subject.	3
7)	It can provide health education and counseling services for women's reproductive health, women of all walks of life and all ages.	3
8)	Performs midwifery practices in line with evidence-based care principles.	3
9)	It establishes positive communication / cooperation with the team in health related practices and can work as an effective team member.	2
10)	It acts in accordance with the ethical principles and values of midwifery profession.	2

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Presentation	1	% 40
Final	1	% 60
total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
total		% 100

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	16	48
Study Hours Out of Class	16	53
Presentations / Seminar	5	10
Final	1	2
Total Workload		113