Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
ARTIFICIAL INTELLIGENCE | BPR2214994 | Spring Semester | 3+0 | 3 | 5 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | Turkish |
Course Level | Short Cycle (Associate's Degree) |
Course Type | Elective |
Course Coordinator | Lect. Beyza KOYULMUŞ |
Name of Lecturer(s) | Lect. Beyza KOYULMUŞ |
Assistant(s) | |
Aim | The aim of this course is to introduce and teach the fundamentals of Artificial Intelligence applications. |
Course Content | This course contains; Introduction to Artificial Intelligence,Philosophy and History of Artificial Intelligence,Basic Concepts,Problem Solving with Artificial Intelligence,Machine Learning,Unsupervised, Supervised and Reinforcement Learning,Big Data and Computing Technology,Intelligent Agents,Deep Learning,Neural Networks,Natural Language Processing,Computer Vision,Predictive models and sample applications,The Future of Artificial Intelligence. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Knows the concepts of Artificial Intelligence. | 10, 16, 9 | A, E, H |
Knows the types of machine learning. | 10, 16, 9 | A, E |
Knows the application areas of machine learning. | 10, 16, 9 | A, E, F |
Knows the concepts of big data and computing technology. | 16, 23, 9 | A, E, F, G |
Conducts current research in the field of artificial intelligence | 16, 9 | A, E, G |
Understands the basics of artificial intelligence | 10, 16, 9 | A, E |
Teaching Methods: | 10: Discussion Method, 16: Question - Answer Technique, 23: Concept Map Technique, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz, H: Performance Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Artificial Intelligence | |
2 | Philosophy and History of Artificial Intelligence | |
3 | Basic Concepts | |
4 | Problem Solving with Artificial Intelligence | |
5 | Machine Learning | |
6 | Unsupervised, Supervised and Reinforcement Learning | |
7 | Big Data and Computing Technology | |
8 | Intelligent Agents | |
9 | Deep Learning | |
10 | Neural Networks | |
11 | Natural Language Processing | |
12 | Computer Vision | |
13 | Predictive models and sample applications | |
14 | The Future of Artificial Intelligence | |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business. | | | | X | |
2 | Chooses and uses the proper solution methods and special techniques for programming purpose. | | | X | | |
3 | Uses modern techniques and tools for programming applications. | | | | X | |
4 | Works effectively individually and in teams. | | | X | | |
5 | Implements and follows test cases of developed software and applications. | | | | X | |
6 | Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices. | | X | | | |
7 | Reaches information, and surveys resources for this purpose. | | | | X | |
8 | Aware of the necessity of life-long learning; follows technological advances and renews him/herself. | | | | X | |
9 | Communicates, oral and written, effectively using modern tools. | | X | | | |
10 | Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance. | | | | | X |
11 | Keeps attention in clean and readable code design. | | X | | | |
12 | Considers and follows user centered design principles. | X | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 40 |
Rate of Final Exam to Success | | 60 |
Total | | 100 |
ECTS / Workload Table |
Activities | Number of | Duration(Hour) | Total Workload(Hour) |
Course Hours | 0 | 0 | 0 |
Guided Problem Solving | 0 | 0 | 0 |
Resolution of Homework Problems and Submission as a Report | 0 | 0 | 0 |
Term Project | 0 | 0 | 0 |
Presentation of Project / Seminar | 0 | 0 | 0 |
Quiz | 0 | 0 | 0 |
Midterm Exam | 0 | 0 | 0 |
General Exam | 0 | 0 | 0 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 0 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(0/30) | 0 |
ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
ARTIFICIAL INTELLIGENCE | BPR2214994 | Spring Semester | 3+0 | 3 | 5 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | Turkish |
Course Level | Short Cycle (Associate's Degree) |
Course Type | Elective |
Course Coordinator | Lect. Beyza KOYULMUŞ |
Name of Lecturer(s) | Lect. Beyza KOYULMUŞ |
Assistant(s) | |
Aim | The aim of this course is to introduce and teach the fundamentals of Artificial Intelligence applications. |
Course Content | This course contains; Introduction to Artificial Intelligence,Philosophy and History of Artificial Intelligence,Basic Concepts,Problem Solving with Artificial Intelligence,Machine Learning,Unsupervised, Supervised and Reinforcement Learning,Big Data and Computing Technology,Intelligent Agents,Deep Learning,Neural Networks,Natural Language Processing,Computer Vision,Predictive models and sample applications,The Future of Artificial Intelligence. |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Knows the concepts of Artificial Intelligence. | 10, 16, 9 | A, E, H |
Knows the types of machine learning. | 10, 16, 9 | A, E |
Knows the application areas of machine learning. | 10, 16, 9 | A, E, F |
Knows the concepts of big data and computing technology. | 16, 23, 9 | A, E, F, G |
Conducts current research in the field of artificial intelligence | 16, 9 | A, E, G |
Understands the basics of artificial intelligence | 10, 16, 9 | A, E |
Teaching Methods: | 10: Discussion Method, 16: Question - Answer Technique, 23: Concept Map Technique, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task, G: Quiz, H: Performance Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Artificial Intelligence | |
2 | Philosophy and History of Artificial Intelligence | |
3 | Basic Concepts | |
4 | Problem Solving with Artificial Intelligence | |
5 | Machine Learning | |
6 | Unsupervised, Supervised and Reinforcement Learning | |
7 | Big Data and Computing Technology | |
8 | Intelligent Agents | |
9 | Deep Learning | |
10 | Neural Networks | |
11 | Natural Language Processing | |
12 | Computer Vision | |
13 | Predictive models and sample applications | |
14 | The Future of Artificial Intelligence | |
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | Has the background in algorithms, programming, and application development in software engineering projects; and has the ability to use them together in business. | | | | X | |
2 | Chooses and uses the proper solution methods and special techniques for programming purpose. | | | X | | |
3 | Uses modern techniques and tools for programming applications. | | | | X | |
4 | Works effectively individually and in teams. | | | X | | |
5 | Implements and follows test cases of developed software and applications. | | | | X | |
6 | Has the awareness in workplace practices, worker health, environmental and workplace safety, professional and ethical responsibility, and legal issues about programming practices. | | X | | | |
7 | Reaches information, and surveys resources for this purpose. | | | | X | |
8 | Aware of the necessity of life-long learning; follows technological advances and renews him/herself. | | | | X | |
9 | Communicates, oral and written, effectively using modern tools. | | X | | | |
10 | Aware of universal and social effects of software solutions and practices; develops new software tools for solving universal problems and social advance. | | | | | X |
11 | Keeps attention in clean and readable code design. | | X | | | |
12 | Considers and follows user centered design principles. | X | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 40 |
Rate of Final Exam to Success | | 60 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 05/11/2023 - 20:23Son Güncelleme Tarihi: 05/11/2023 - 20:25
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