Computer Technology Preview
Associate	TR-NQF-HE: Level 5	QF-EHEA: Short Cycle	EQF-LLL: Level 5

Course Introduction and Application Information

Course Code:

UNI220

Course Name:

Machine Learning and Data Science

Semester:

Fall
Spring

Course Credits:

ECTS

Language of instruction:

Turkish

Course Condition:

Does the Course Require Work Experience?:

Type of course:

University Elective

Course Level:

Associate

TR-NQF-HE:5. Master`s Degree

QF-EHEA:Short Cycle

EQF-LLL:5. 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) To possess the ability to comprehend fundamental concepts in the field of computer technologies.
2) To possess the ability to analyze and model problems in the field of computer technologies, and to determine and define appropriate software requirements for their solutions.
3) To possess the ability to comprehend professional topics in a foreign language.
4) To possess the capability to perform software installation, testing, and acceptance procedures.
5) To possess the ability to carry out the setup and administration of computer networks, as well as utilize network operating systems.
6) To possess the skill of conducting maintenance and repairs on computer hardware.
7) To have awareness and responsibility regarding professional, legal, ethical, and social issues in the field of computer technologies.
8) To possess the capability to plan, design, and develop computer and network software.
9) To possess the ability to design and program for web development.
10) To be competent in applying knowledge of database management, querying, and design to practical applications, and to be capable of developing database applications.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	To possess the ability to comprehend fundamental concepts in the field of computer technologies.
2)	To possess the ability to analyze and model problems in the field of computer technologies, and to determine and define appropriate software requirements for their solutions.
3)	To possess the ability to comprehend professional topics in a foreign language.
4)	To possess the capability to perform software installation, testing, and acceptance procedures.
5)	To possess the ability to carry out the setup and administration of computer networks, as well as utilize network operating systems.
6)	To possess the skill of conducting maintenance and repairs on computer hardware.
7)	To have awareness and responsibility regarding professional, legal, ethical, and social issues in the field of computer technologies.
8)	To possess the capability to plan, design, and develop computer and network software.
9)	To possess the ability to design and program for web development.
10)	To be competent in applying knowledge of database management, querying, and design to practical applications, and to be capable of developing database applications.

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