SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code Course Name Year Semester Theoretical Practical Credit ECTS
50613YETOS-BIT2012 Artificial Intelligence for Everyone 1 Spring 2 0 2 3
Course Type : University Elective
Cycle: Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction: Turkish
Prerequisities and Co-requisities: N/A
Mode of Delivery: Face to face
Name of Coordinator: Instructor AYŞE BERİKA VAROL MALKOÇOĞLU
Dersin Öğretim Eleman(lar)ı:

Dersin Kategorisi: Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives: The aim of this course is to raise awareness about the concept of artificial intelligence, its processes and basic techniques.
Course Content: 1. Explains artificial intelligence.
2. Knows the historical development and philosophy of artificial intelligence.
3. Describes the usage areas of artificial intelligence in different disciplines.
4. Explains the analysis logic of artificial intelligence.
5. Explains the aims of artificial intelligence and the methods it uses to achieve these goals.
6. Explains social, technological and economic change with artificial intelligence.
7. Knows the relationship between artificial intelligence and ethics.
8. Express the development process of future artificial intelligence.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
Knowledge (Described as Theoritical and/or Factual Knowledge.)
Skills (Describe as Cognitive and/or Practical Skills.)
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Weekly Course Schedule

Week Subject
Materials Sharing *
Related Preparation Further Study
1) What is Artificial Intelligence? Historical Development and Philosophy
2) Change Process with Artificial Intelligence (Profession change, social changes)
3) Artificial Intelligence Application Areas (Natural Language processing, computer vision, decision making, problem solving, voice recognition…)
4) How Artificial Intelligence Learns?
5) Big Data (Data Mining, Text Mining, Learning Analytics…)
6) Programs Used in Artificial Intelligence Applications and Application
7) Uses and Examples of Artificial Intelligence I (Architecture)
8) MIDTERM EXAM
9) Uses and Examples of Artificial Intelligence II (Health)
10) Uses and Examples of Artificial Intelligence III (Cyber Security)
11) Uses and Examples of Artificial Intelligence IV (Art)
12) Uses and Examples of Artificial Intelligence V (Education)
13) Artificial Intelligence and Ethics
14) Artificial Intelligence in the Near Future
*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References:

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

(The matrix below shows how the course learning outcomes (CLOs) associates with programme learning outcomes (both KPLOs & SPLOs) and, if exist, the level of quantitative contribution to them.)

Relationship Between CLOs & PLOs

(KPLOs and SPLOs are the abbreviations for Key & Sub- Programme Learning Outcomes, respectively. )
CLOs/PLOs KPLO 1 KPLO 2 KPLO 4 KPLO 5
1 2 1 2 3 4 5 6 7 8 9 10 1 2 3 4 1 2 3 4 5 6 7

Level of Contribution of the Course to PLOs

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Programme Learning Outcomes Contribution Level (from 1 to 5)
1) Have basic, theoretical and applied knowledge about E-Commerce.
2) Have basic, theoretical and applied knowledge about Marketing.
3) Learns the basics of web design.
4) Applies the theoretical knowledge learned by being in business life for one semester.
5) Gains at a basic level the competencies that develop in line with the expectations of the business world and society and are defined as the institutional outputs of our university.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Teaching and Learning Methods defined at the Programme Level
Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Case Study
Problem Solving
Demonstration
Views
Laboratory
Reading
Homework
Project Preparation
Thesis Preparation
Peer Education
Seminar
Technical Visit
Course Conference
Brain Storming
Questions Answers
Individual and Group Work
Role Playing-Animation-Improvisation
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Aassessment and evaluation Methods defined at the Programme Level
Assessment and Evaluation Methods defined for the Course
Midterm
Presentation
Final Exam
Quiz
Report Evaluation
Homework Evaluation
Oral Exam
Thesis Defense
Jury Evaluation
Practice Exam
Evaluation of Implementation Training in the Workplace
Active Participation in Class
Participation in Discussions

Relationship Between CLOs & Teaching-Learning, Assesment-Evaluation Methods of the Course

(The matrix below shows the teaching-learning and assessment-evaluation methods designated for the course unit in relation to the course learning outcomes.)
LEARNING & TEACHING METHODS
COURSE LEARNING OUTCOMES
ASSESMENT & EVALUATION METHODS
-Lectures -Midterm
-Discussion -Presentation
-Case Study -Final Exam
-Problem Solving -Quiz
-Demonstration -Report Evaluation
-Views -Homework Evaluation
-Laboratory -Oral Exam
-Reading -Thesis Defense
-Homework -Jury Evaluation
-Project Preparation -Practice Exam
-Thesis Preparation -Evaluation of Implementation Training in the Workplace
-Peer Education -Active Participation in Class
-Seminar - Participation in Discussions
-Technical Visit
-Course Conference
-Brain Storming
-Questions Answers
-Individual and Group Work
-Role Playing-Animation-Improvisation
-Active Participation in Class

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods # of practice per semester Level of Contribution
Quizzes 2 % 20.00
Midterms 1 % 30.00
Semester Final Exam 1 % 50.00
Total % 100
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
Total % 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE