Architecture (English) 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:

Fall

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) She/he gains knowledge of and develops values on technical, aesthetic, cultural, historical, social and ethical dimensions of architecture with a scientific and critical approach.
2) She/he integrates architectural practice with environmental, economic and social sustainability principles.
3) She/he has the knowledge and ability to provide and implement interactions between urban planning, urban design and architectural projects.
4) Gains the ability to identify architectural potentials and problems based on data collection, analysis, interpretation and critical thinking, in order to cultivate concepts and determine strategies for action.
5) She/he is able to interrelate theory, design and construction practices.
6) She/he will be able to produce architectural design, presentation, implementation, management and supervision stages both independently and collectively for different contexts and scales and through a responsive approach to social, functional, technical and aesthetic requirements.
7) In addition to traditional methods, she/he interactively uses the emerging information technologies required by the field.
8) To analyze and document the historical and conservation characteristics of the built environment; taking into account of the balance between protection and use, she/he has the ability and necessary knowledge in renovation and restoration issues.
9) She / he gains the ability to cooperate with different disciplines on the conception and design of the built environment, as an individual and/or as a team member.
10) Has knowledge on and comprehension of professional ethics and codes of conduct, legal and managerial regulations, standards, rights and responsibilities and processes in the field of architecture.
11) Can produce design, know-how and knowledge for the improvement of different and changing social needs, and for the enhancement of life quality.
12) She/he has the knowledge and responsibility to design solidly built structures and takes into account of the risks of natural disaster.
13) She/he monitors new developments in architectural theory and practice and is open to lifelong learning.
14) She/he takes responsibility for the improvement of social consciousness in the field of architecture, and for the endorsement and defense of ecological and urban rights.
15) Has architectural communication skills in a foreign language.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	She/he gains knowledge of and develops values on technical, aesthetic, cultural, historical, social and ethical dimensions of architecture with a scientific and critical approach.
2)	She/he integrates architectural practice with environmental, economic and social sustainability principles.
3)	She/he has the knowledge and ability to provide and implement interactions between urban planning, urban design and architectural projects.
4)	Gains the ability to identify architectural potentials and problems based on data collection, analysis, interpretation and critical thinking, in order to cultivate concepts and determine strategies for action.
5)	She/he is able to interrelate theory, design and construction practices.
6)	She/he will be able to produce architectural design, presentation, implementation, management and supervision stages both independently and collectively for different contexts and scales and through a responsive approach to social, functional, technical and aesthetic requirements.
7)	In addition to traditional methods, she/he interactively uses the emerging information technologies required by the field.
8)	To analyze and document the historical and conservation characteristics of the built environment; taking into account of the balance between protection and use, she/he has the ability and necessary knowledge in renovation and restoration issues.
9)	She / he gains the ability to cooperate with different disciplines on the conception and design of the built environment, as an individual and/or as a team member.
10)	Has knowledge on and comprehension of professional ethics and codes of conduct, legal and managerial regulations, standards, rights and responsibilities and processes in the field of architecture.
11)	Can produce design, know-how and knowledge for the improvement of different and changing social needs, and for the enhancement of life quality.
12)	She/he has the knowledge and responsibility to design solidly built structures and takes into account of the risks of natural disaster.
13)	She/he monitors new developments in architectural theory and practice and is open to lifelong learning.
14)	She/he takes responsibility for the improvement of social consciousness in the field of architecture, and for the endorsement and defense of ecological and urban rights.
15)	Has architectural communication skills in a foreign language.

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