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| Knowledge
(Described as Theoritical and/or Factual Knowledge.)
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1) Artificial Intelligence and Machine Learning Concepts: Understanding the basic principles of artificial intelligence (AI) and machine learning (ML) concepts and the differences between them.
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2) Artificial Intelligence History and Development: Understand the historical development of AI, its important milestones, and how the field has evolved.
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3) Artificial Intelligence Application Areas: Learning examples of AI applications in different sectors such as health, education, industry and finance.
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4) Algorithms and Models: Understand basic AI and ML algorithms, models, and when to use them.
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| Skills
(Describe as Cognitive and/or Practical Skills.)
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1) Problem Solving: Ability to apply AI and ML techniques to solve real world problems.
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2) Algorithm Development and Implementation: Ability to code and test basic AI algorithms in a programming language such as Python.
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3) Data Analysis: Ability to collect, clean and analyze data; Being able to extract meaningful information from data.
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4) Model Training and Evaluation: The ability to train and test AI models and evaluate their performance.
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| 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.)
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1) Critical Thinking: The ability to critically evaluate the ethical, social and legal dimensions of AI solutions.
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2) Teamwork: Ability to work effectively in diverse teams and collaborate on projects.
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3) Lifelong Learning: Commitment to continuous learning and keeping pace with the rapid evolution of AI and BC.
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4) Innovation and Creativity: Ability to develop innovative solutions and encourage creative thinking using AI technologies.
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| Week |
Subject |
| Related Preparation |
Pekiştirme |
| 1) |
Introduction to Artificial Intelligence and Basic Concepts |
| 2) |
Artificial Intelligence Application Areas |
| 3) |
Problem Solving Approach |
| 4) |
Intuitive Search and Analysis |
| 5) |
Artificial Neural Networks |
| 6) |
Convolutional Neural Networks |
| 7) |
Recurrent Neural Networks |
| 8) |
Learning Methods |
| 9) |
Machine Learning |
| 10) |
Natural Language Processing |
| 11) |
Artificial Intelligence in Games |
| 12) |
Data Modeling and Inference |
| 13) |
Pattern and Biometric Recognition |
| 14) |
Robotics |
| References: |
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig |
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| Q & A |
|
| Case Problem Solving/ Drama- Role/ Case Management |
|
| Laboratory |
|
| Quantitative Problem Solving |
|
| Fieldwork |
|
| Group Study / Assignment |
|
| Individual Assignment |
|
| WEB-based Learning |
|
| Internship |
|
| Practice in Field |
|
| Project Preparation |
|
| Report Writing |
|
| Seminar |
|
| Supervision |
|
| Social Activity |
|
| Occupational Activity |
|
| Occupational Trip |
|
| Application (Modelling, Design, Model, Simulation, Experiment et.) |
|
| Reading |
|
| Thesis Preparation |
|
| Field Study |
|
| Student Club and Council Activities |
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| Other |
|
| Logbook |
|
| Interview and Oral Conversation |
|
| Research |
|
| Watching a movie |
|
| Bibliography preparation |
|
| Oral, inscribed and visual knowledge production |
|
| Taking photographs |
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| Sketching |
|
| Mapping and marking |
|
| Reading maps |
|
| Copying textures |
|
| Creating a library of materials |
|
| Presentation |
|
| Semester Requirements |
Number of Activities |
Level of Contribution |
| Attendance |
|
% 0 |
| Laboratory |
|
% 0 |
| Application |
|
% 0 |
| Practice Exam |
|
% 0 |
| Quizzes |
|
% 0 |
| Homework Assignments |
|
% 0 |
| Presentation |
|
% 0 |
| Project |
|
% 0 |
| Special Course Internship (Work Placement) |
|
% 0 |
| Field Study |
|
% 0 |
| Article Critical |
|
% 0 |
| Article Writing |
|
% 0 |
| Module Group Study |
|
% 0 |
| Brainstorming |
|
% 0 |
| Role Playing + Dramatizing |
|
% 0 |
| Out of Class Study |
|
% 0 |
| Preliminary Work, Reinforcement |
|
% 0 |
| Application Repetition etc. |
|
% 0 |
| Homework (reading, writing, watching movies, etc.) |
|
% 0 |
| Project Preparation + Presentation |
|
% 0 |
| Report Preparation + Presentation |
|
% 0 |
| Presentation / Seminar Preparation + Presenting |
|
% 0 |
| Oral examination |
|
% 0 |
| Midterms |
1 |
% 40 |
| Final |
1 |
% 60 |
| Report Submission |
|
% 0 |
| Bütünleme |
|
% 0 |
| Kanaat Notu |
|
% 0 |
| Committee |
|
% 0 |
| Yazma Ödev Dosyası |
|
% 0 |
| Portfolio |
|
% 0 |
| Take-Home Exam |
|
% 0 |
| Logbook
|
|
% 0 |
| Discussion |
|
% 0 |
| Participation |
|
% 0 |
| total |
% 100 |
| PERCENTAGE OF SEMESTER WORK |
|
% 40 |
| PERCENTAGE OF FINAL WORK |
|
% 60 |
| total |
% 100 |
| Activities |
Number of Activities |
Workload |
| Course Hours |
14 |
28 |
| Laboratory |
|
|
| Application |
|
|
| Practice Exam |
|
|
| Special Course Internship (Work Placement) |
|
|
| Field Work |
|
|
| Study Hours Out of Class |
|
|
| Article Critical |
|
|
| Article Writing |
|
|
| Module Group Study |
|
|
| Brainstorming |
|
|
| Role Playing + Dramatizing |
|
|
| Out-of-Class Study (Pre-study, Reinforcement, Practice Review, etc.) |
14 |
28 |
| Homework (reading, writing, watching movies, etc.) |
|
|
| Project Preparation + Presentation |
|
|
| Report Preparation + Presentation |
|
|
| Presentation / Seminar Preparation + Presenting |
|
|
| Oral examination |
|
|
| Preparing for Midterm Exams |
7 |
14 |
| MIDTERM EXAM (Visa) |
1 |
1 |
| Preparing for the General Exam |
14 |
28 |
| GENERAL EXAM (Final) |
1 |
1 |
| Participation |
|
|
| Discussion |
|
|
| Portfolio |
|
|
| Take-Home Exam |
|
|
| Logbook
|
|
|
| Total Workload |
100 |
| ECTS (30 saat = 1 AKTS ) |
3 |
