VOCATIONAL SCHOOL
Department of Computer Programming (Turkish)
MBP 251 | Course Introduction and Application Information
| Course Name |
Artificial Intelligence and Blockchain
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
MBP 251
|
Fall/Spring
|
2
|
2
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
Turkish
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Short Cycle
|
|||||
| Mode of Delivery | face to face | |||||
| Teaching Methods and Techniques of the Course | Group WorkLecture / Presentation | |||||
| Course Coordinator | - | |||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course aims to introduce students to the basic concepts and methods in Blockchain and Artificial Intelligence. In addition, it is aimed that students develop Python applications in machine learning and deep learning. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course includes the basic concepts and methods of Blockchain and artificial intelligence, machine learning, deep learning, Python programming language, artificial neural networks. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Blockchain Introduction, Basic Concepts, Types | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 1 |
| 2 | Introduction to the Blockchain World Money Concept and Cryptocurrencies | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 2 |
| 3 | Blockchain Application Areas, Application Examples What is Bitcoin, Other Tokens | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 2 |
| 4 | ICO Concept and Details Cryptology, Smart Contracts, Project 1 Brief | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 3 |
| 5 | Artificial Intelligence - Introduction, Topics, Definitions, History, Philosophy, Agents | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 4 |
| 6 | Artificial Intelligence - Problem Solving and Search Algorithms, Knowledgeable-Uninformed Search, Natural Language Processing | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 5 |
| 7 | Artificial Intelligence - Genetic Algorithms Game Problems | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 6 |
| 8 | Project Deadline (Midterm) | |
| 9 | Student Project presentations Project 2 Brief | |
| 10 | Machine Learning, Introduction and Definitions, Applications, Examples | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 8 |
| 11 | Deep Learning, Introduction and Definitions, Applications, Examples | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 9 |
| 12 | Artificial Neural Networks, Introduction and Definitions, Data Types, Examples | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 10 |
| 13 | Artificial Intelligence and Python Programming Example applications | Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi Part 8-10 |
| 14 | Project Presentations | |
| 15 | Review of Semester | |
| 16 | Project Evaluation |
| Course Notes/Textbooks | - Vasif Nabiyev, Yapay Zeka: İnsan ve Bilgisayar Etkileşimi 4. Baskı, Seçkin Yayıncılık ISBN : 9789750220340 |
| Suggested Readings/Materials | - Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems (3rd Edition) 3rd Edition ISBN : 978-1408225745 - Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Third Ed., Prentice Hall, 2010, ISBN : 978-0136042594 |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
20
|
| Presentation / Jury |
1
|
20
|
| Project |
2
|
60
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam | ||
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
100
|
| Weighting of End-of-Semester Activities on the Final Grade | ||
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
2
|
32
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
2
|
32
|
| Study Hours Out of Class |
14
|
1
|
14
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
10
|
10
|
| Presentation / Jury |
1
|
20
|
20
|
| Project |
2
|
20
|
40
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
0
|
||
| Total |
148
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to have basic computer hardware and software knowledge. |
X | ||||
| 2 | To be able to develop the necessary applications by using internet and network technologies. |
X | ||||
| 3 | To follow developments in the field to adapt to changing conditions. |
X | ||||
| 4 | To be able to conduct experiments in the field and analyze the results. |
|||||
| 5 | To be able to use basic programming languages related to the field. |
X | ||||
| 6 | To be able to design and install a computer system that includes software, hardware, or both, meeting the basic needs of the field. |
X | ||||
| 7 | To be able to interpret and follow current developments in the field of computer programming. |
X | ||||
| 8 | To be able to carry professional and ethical responsibility and have awareness of professional ethics in their practices. |
|||||
| 9 | To have basic theoretical and practical knowledge about mathematics, computing and computer science. |
|||||
| 10 | To be able to follow the information in the field and communicate with colleagues by using English at the general level of European Language Portfolio A2. |
|||||
| 11 | To be able to direct his/her education to a further level of education |
|||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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