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Information
| Code | BPP255 |
| Name | Artificial intelligence |
| Term | 2024-2025 Academic Year |
| Semester | 3. Semester |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Ön Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör. Alişan AKTAY |
| Course Instructor |
Öğr. Gör.Dr. Cevher ÖZDEN
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
To create a general culture on artificial intelligence concepts and techniques, to gain the knowledge and skills to analyze and write programs at the basic level with artificial neural networks, deep networks and other intelligent techniques. Basic knowledge of Python artificial intelligence libraries.
Course Content
Basic artificial intelligence, machine learning, general structure of deep learning algorithms and systems, expert systems, artificial neural networks, deep learning, optimization algorithms, logic and fuzzy logic, programming languages for artificial intelligence.
Course Precondition
None
Resources
Apaydın, E. Artificial Learning: New Artificial Intelligence, 2020. Deep Learning with Applications Using Python, Apress, 2018 India Wirsansky E, Hands On Genetics Algorithms with Python, Packt, 2020, Birmingham Özgül. F, Aspects of Python, Kodlab, 2020, Sevli, O., Python 3
Notes
https://neuralnetworksanddeeplearning.com/index.html, KTU lecture notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the concept of artificial intelligence and its algorithms. |
| LO02 | Express the concepts of artificial neural networks and deep learning |
| LO03 | Refers to computer vision and convolutional neural networks. |
| LO04 | Knows and expresses recurrent neural networks. |
| LO05 | Writes basic programs in programming languages (Python, Matlab etc.) using artificial intelligence. |
| LO06 | Implements artificial intelligence algorithms in a chosen programming language. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | explains the basic and scientific concepts related to computer technologies. | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Explains the hardware structures and the functions and functions of the electronic circuit elements that make up these hardware structures | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Uses basic concepts in the field of computer technologies and Office programs and various package programs | |
| PLO04 | Bilgi - Kuramsal, Olgusal | He/She has the ability to apply and solve problems in the field of computer programming by developing algorithms with software languages and utilities. | 4 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Explain the basic concepts of computer hardware structures, make simple software installations and various hardware configurations, | |
| PLO06 | Bilgi - Kuramsal, Olgusal | designs basic database systems and database programs. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Uses basic graphic and animation programs used to design interfaces on web pages | |
| PLO08 | Bilgi - Kuramsal, Olgusal | Explains and designs network systems, their types and makes simple installation examples. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Knows and uses internet technologies and develops server-side internet applications. | |
| PLO10 | Bilgi - Kuramsal, Olgusal | Knows various computer programming languages (Delphi, Visual Basic, C++ etc.). | 4 |
| PLO11 | Bilgi - Kuramsal, Olgusal | He/she can carry out and conclude a basic study related to his/her field independently or in disciplined teams | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Perceives and uses new technologies in the field with the necessity of lifelong learning | |
| PLO13 | Bilgi - Kuramsal, Olgusal | He/She knows a foreign language (professional foreign language) at A2 level, sufficient for the applications in her field. | |
| PLO14 | Bilgi - Kuramsal, Olgusal | Able to communicate verbally and in writing by using Turkish effectively. Asks questions, makes observations, thinks critically and constructively, abides by the principles of academic honesty, is entrepreneurial. | |
| PLO15 | Bilgi - Kuramsal, Olgusal | Shares designs and applications related to computer technologies with colleagues, can clearly explain this information to other people | 1 |
| PLO16 | Bilgi - Kuramsal, Olgusal | She/He is conscious and knowledgeable about Atatürk's Principles and the History of the Revolution. | |
| PLO17 | Bilgi - Kuramsal, Olgusal | It is aware of occupational health and safety, environment and ethical values within the framework of global and social values. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to artificial learning. Historical development and application areas | Examination of source books. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Supervised, unsupervised learning | Basic knowledge of machine learning. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Artificial neural networks and basic concepts | Researching the basic concepts of artificial neural networks | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Structures of Artificial Neural Networks | Researching artificial neural network layers and their functions | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | The most commonly used Hyper-parameters in neural networks | Investigation of types of hyper-parameters | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Deep learning | Learn about deep learning | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Deep learning application | Researching deep learning tools or programming languages | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
|
| 9 | Computer Vision | Researching computer vision concepts | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Convolutional neural networks | Researching convolutional neural networks | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Basic Convolutional neural networks application | Image search to be used in convolutional neural networks applications | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Convolutional neural networks, their constraints, future and applications | Researching apps | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Recurrent neural networks | Recurrent neural networks research | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 14 | Types of recurrent neural networks, areas of use | Recurrent neural networks research | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | Application of recurrent neural networks | Researching tools and programming languages | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 81 | ||
| Total Workload / 25 (h) | 3,24 | ||
| ECTS | 3 ECTS | ||