NEURAL NETWORKS AND DEEP LEARNING
- Course
- DASC488 - NEURAL NETWORKS AND DEEP LEARNING
- Department
- Data Science - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 6
- T+P+L
- 3 + 0 + 1
- Course Coordinator(s)
- Prof. Dr. Tolgay KARANFİLLER
- Prerequisite
- -
Course Description
In this course Artificial Neural Network (ANN), which is a mathematical paradigm imitating biological neural network for reasoning and problem solving is covered. With focus on practice, the course cover ANN models for various real-world applications and offers a hands-on introduction to deep learning tools and techniques. Students do not need to have an extensive math background to understand this course. The course covers McCulloch-Pitts model and basic neural network models, multilayer perceptron, associative memory, self-organizing feature maps, recurrent neural networks, etc. and reviews applications of these models to various types of data. Upon completion of this course, students will gain a broad understanding of the context of neural networks and deep learning, the data demands of deep learning and the parameters for neural networks.
NEURAL NETWORKS AND DEEP LEARNING
Evaluation Tools (Active Term)
| Item | Type | Weight (%) |
|---|---|---|
| Kısa Sınav 1 | Quiz | 15 |
| Kısa Sınav 2 | Quiz | 15 |
| Vize | Midterm | 30 |
| Final | Final | 40 |
| Total | 100 | |
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Artificial Neural Networks |
| Week 2 | Basic Neuron Models — The McCulloch–Pitts Model |
| Week 3 | The Perceptron and Its Learning Rule |
| Week 4 | ADALINE / Delta Rule and Its Limitations |
| Week 5 | The Multilayer Perceptron (MLP) |
| Week 6 | Training the MLP and First Hands-On Model |
| Week 7 | Deep Learning Tools and Techniques |
| Week 8 | Midterm Exam Week |
| Week 9 | Midterm Exam Week |
| Week 10 | Convolutional Neural Networks (CNN) |
| Week 11 | Recurrent Neural Networks (RNN) |
| Week 12 | Advanced Sequence Modeling with LSTM / GRU |
| Week 13 | Associative Memory — Hopfield Networks |
| Week 14 | Self-Organizing Feature Maps (SOM / Kohonen) |
| Week 15 | Week 13: Applications, Data Requirements, and Course Review |
Reference Books & Course Materials
- 01 Neural Networks and Deep Learning: A Textbook (2. Baskı, 2023) Charu C. Aggarwal · Springer
- 02 Deep Learning with Python (2. Baskı, 2021) François Chollet (Keras'ın yaratıcısı) · Manning
- 03 Grokking Deep Learning (2019,) Andrew Trask · Manning
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Create a user interface in a contemporary object-oriented language to allow users to access business data
- P02 Identify and analyze user needs and take them into account in the selection, creation, integration, evaluation and administration of computing-based systems
- P03 Analyze common business functions and identify, design, and develop appropriate information technology solutions
- P04 Design and develop software solutions for contemporary business environments by employing appropriate problem-solving strategies
- P05 Configure and administer database server to support contemporary business environments.
- P06 Administer or mange a relational database for a small to medium size company
- P07 Be able to effectively integrate IT-based solutions into the user environment
- P08 Understand professional, ethical, legal, security and social issues and responsibilities
- P09 Be able to apply effective communication skills consistent with the professional environment
- P10 Be able to apply effective collaboration skills in teamwork consistent with the professional environment
Po-Lo Matrix
The PO-LO matrix has not been populated yet.