BİLGİ PAKETİ / DERS KATALOĞU
| COMPUTER ENGINEERING (MASTER) (THESIS) | |||||
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| Qualification Awarded | Program Süresi | Toplam Kredi (AKTS) | Öğretim Şekli | Yeterliliğin Düzeyi ve Öğrenme Alanı | |
| Master's ( Second Cycle) Degree | 2 | 120 | FULL TIME |
TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TR-NQF-HE, ISCED (1997-2013): 48,52 |
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Ders Genel Tanıtım Bilgileri
| Course Code: | 3016002053 | ||||||||||
| Ders İsmi: | Artificial neural networks | ||||||||||
| Ders Yarıyılı: |
Fall |
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| Ders Kredileri: |
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| Language of instruction: | TR | ||||||||||
| Ders Koşulu: | |||||||||||
| Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||||
| Other Recommended Topics for the Course: | To enhance the success of the course and ensure an effective learning experience for students, several additional considerations are recommended. Firstly, it is important that students have a foundational understanding of mathematical concepts, programming skills, or prior knowledge of machine learning to grasp the course content effectively. Additionally, students should be introduced to the software and tools used in the course, such as TensorFlow, PyTorch, and Jupyter Notebook. Clear evaluation methods should be outlined, including criteria such as exams, assignments, and project reports. A comprehensive list of resources and readings, including core and supplementary materials, should be provided to enable students to explore the topics in depth. Practical projects at the end of the course should be designed to apply theoretical knowledge in real-world scenarios. Regular review and updates of the course content are necessary to keep it current. Support and advisory services should be available, including individual consultation hours or additional study sessions. Lastly, ethical and legal issues related to artificial intelligence and data use should be addressed to help students understand their responsibilities in this field. These considerations will support a thorough learning process, both theoretically and practically, for the students. | ||||||||||
| Type of course: | Anabilim Dalı/Lisansüstü Seçmeli | ||||||||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||||||||
| Course Coordinator : | Dr.Öğr.Üyesi Recep DURANAY | ||||||||||
| Course Lecturer(s): |
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| Course Assistants: |
Dersin Amaç ve İçeriği
| Course Objectives: | The aim of this course is to teach students the fundamental concepts and advanced techniques in the field of artificial neural networks and deep learning. The course seeks to provide students with a deep understanding of the mathematical foundations, modeling techniques, and training algorithms that are integral to neural networks, one of the most crucial components of artificial intelligence and machine learning. Students will learn the architectures, activation functions, and training methods of neural networks through hands-on practice and will explore advanced deep learning approaches such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This course aims to equip students with the knowledge and skills necessary to conduct complex data analyses using neural networks and to apply these technologies to real-world problems across various disciplines. |
| Course Content: | This course aims to provide students with a comprehensive understanding of artificial neural networks, starting from fundamental principles and extending to advanced deep learning techniques. The content covers the history and role of neural networks within artificial intelligence, mathematical foundations, and activation functions. Students will gain detailed knowledge of artificial neurons and network architectures, and will explore advanced neural network structures and learning algorithms. Topics include deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. The course will also address how these technologies can be applied to various real-world problems and applications. Additionally, model evaluation and optimization techniques, including model validation and hyperparameter optimization, will be discussed. The course provides opportunities for students to apply their theoretical knowledge through individual or group projects. |
Learning Outcomes
The students who have succeeded in this course;
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Ders Akış Planı
| Week | Subject | Related Preparation |
| 1) | Introduction and Basic Concepts History and development of artificial neural networks Similarities with biological neural networks The role of artificial neural networks within artificial intelligence | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 2) | Mathematical Foundations - Linear Algebra Vectors and matrices Matrix operations and properties Linear transformations | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 3) | Mathematical Foundations - Probability and Statistics Basic probability theory Statistical distributions and moments Data analysis and summarization | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 4) | Mathematical Foundations - Calculus and Optimization Derivatives and integrals Gradient and optimization techniques Finding maximum and minimum points of functions | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 5) | Artificial Neurons and Activation Functions Perceptron model Sigmoid, ReLU, and tanh activation functions Properties and uses of activation functions | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 6) | Neural Network Architectures - Single-Layer and Multi-Layer Networks Single-layer neural networks Multi-layer neural networks (MLP) Structures and application examples | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 7) | Feedforward Neural Networks Structure of feedforward networks Objective functions and learning process How training occurs | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 8) | Midterm exam | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 9) | Training and Learning Algorithms - Backpropagation Backpropagation algorithm Error functions and optimization Learning rate and updates | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 10) | Training and Learning Algorithms - Gradient Descent and Variants Gradient descent and stochastic gradient descent Mini-batch gradient descent Learning rate adjustments and adaptive methods | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 11) | Deep Learning Models - Convolutional Neural Networks (CNN) Basic components of CNNs Convolution and pooling operations CNN application examples and practices | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 12) | Deep Learning Models - Recurrent Neural Networks (RNN) Basic structure of RNNs Short-term and long-term dependencies RNN application examples and practices | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 13) | Deep Learning Models - Long Short-Term Memory (LSTM) Networks Structure and components of LSTM Advantages of LSTM networks Application examples and practices | Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 14) | Model Evaluation and Optimization Techniques Model validation and cross-validation Hyperparameter optimization Performance metrics and evaluation | Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 15) | Applications - Image Processing and Computer Vision Image processing techniques Computer vision applications Image classification using CNNs | Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| 16) | Final exam | Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
Sources
| Course Notes / Textbooks: | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
| References: | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (English) A comprehensive introduction to deep learning, including the mathematics behind neural networks and practical applications. Neural Networks and Deep Learning by Michael Nielsen (English) A free online book that covers the theory and implementation of neural networks. Derin Öğrenme ile Yapay Zeka by İbrahim Çakıroğlu (Turkish) Temel sinir ağı teorileri ve derin öğrenme uygulamaları üzerine bir Türkçe kaynak. Yapay Sinir Ağları ve Derin Öğrenme by Ahmet Üstündağ (Turkish) Yapay sinir ağlarının ve derin öğrenmenin detaylı anlatımı. Pattern Recognition and Machine Learning by Christopher M. Bishop (English) A widely used textbook that includes neural networks and their applications in pattern recognition. |
Ders - Program Öğrenme Kazanım İlişkisi
| Ders Öğrenme Kazanımları | 1 |
2 |
4 |
2 |
5 |
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| Program Outcomes | |||||||||
| 1) Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply. | |||||||||
| 2) Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines. | |||||||||
| 3) Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution. | |||||||||
| 4) Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process. | |||||||||
| 5) Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints. | |||||||||
| 6) Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process. | |||||||||
| 7) Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication. | |||||||||
| 8) Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility. | |||||||||
| 9) Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values. | |||||||||
| 10) Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary. | |||||||||
Ders - Öğrenme Kazanımı İlişkisi
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Ability to reach wide and deep knowledge through scientific research in the field of Computer Science and Engineering, evaluate, interpret and apply. | 4 |
| 2) | Ability to use scientific methods to cover and apply limited or missing knowledge, and to integrate the knowledge of different disciplines. | 4 |
| 3) | Ability to construct Computer Science and Engineering problems, develop methods to solve the problems and use innovative methods in the solution. | 4 |
| 4) | Ability to develop new and/or original ideas and algorithm; develop innovative solutions in the design of system, component or process. | 4 |
| 5) | Ability to have extensive knowledge about current techniques and methods applied in Computer Engineering and their constraints. | 3 |
| 6) | Ability to design and implement analytical modeling and experimental research, solve and interpret complex situations encountered in the process. | 2 |
| 7) | Ability to use a foreign language (English) at least at the level of European Language Portfolio B2 in verbal and written communication. | 3 |
| 8) | Ability to lead in multidisciplinary teams, develop solutions to complex situations and take responsibility. | 4 |
| 9) | Awareness of the social, legal, ethical and moral values, and the ability to conduct research and implementation work within the framework of these values. | 3 |
| 10) | Awareness of the new and emerging applications in Computer Science and Engineering field, and the ability to examine them and learn if necessary. | 5 |
Öğrenme Etkinliği ve Öğretme Yöntemleri
| Course | |
| Okuma |
Ölçme ve Değerlendirme Yöntemleri ve Kriterleri
| Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
| Homework |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 3 | % 60 |
| Semester Final Exam | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
İş Yükü ve AKTS Kredisi Hesaplaması
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 3 | 14 | 42 |
| Application | 2 | 14 | 28 |
| Study Hours Out of Class | 14 | 5 | 70 |
| Homework Assignments | 2 | 10 | 20 |
| Midterms | 3 | 2 | 6 |
| Final | 1 | 3 | 3 |
| Total Workload | 169 | ||