FUNDAMENTALS OF NEURAL NETWORKS
- Course
- AIEN421 - FUNDAMENTALS OF NEURAL NETWORKS
- Department
- Artificial Intelligence Engineering - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- Asst. Prof. Dr. Tolga YIRTICI
- Prerequisite
- -
- Keywords
Course Description
The neural networks course represents the basic neural network architectures and learning algorithms which are inspired from the human brain. Neurocomputing stands as a sub-topic of artificial intelligence together with evolutionary computing. Within the scope of the course, perceptrons and perceptron learning algorithm are introduced. The architectures include Multilayer Perceptrons, Hopfield Model and Self Organized Maps covering Mallberg’s and Kohonen’s models. Learning algorithms will cover the selected supervised and unsupervised learning algorithms including backpropagation, Hebbian learning, competitive learning and the selected clustering algorithms. Several pattern recognition and computer vision applications will be presented as case studies. The applications will be simulated on selected simulators.
FUNDAMENTALS OF NEURAL NETWORKS
Evaluation Tools (Active Term)
| Item | Type | Weight (%) |
|---|---|---|
| Midterm | Midterm | 35 |
| Final | Final | 45 |
| Quiz 1 | Quiz | 10 |
| Quiz 2 | Quiz | 10 |
| Total | 100 | |
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Neural Networks (Biological vs. artificial neurons, history, ML paradigms, applications) |
| Week 2 | Feedforward Neural Networks (Perceptron, MLP, forward propagation, activation functions) |
| Week 3 | Feedforward Neural Networks (Perceptron, MLP, forward propagation, activation functions) |
| Week 4 | Training Neural Networks (Loss functions, gradient descent, backpropagation) |
| Week 5 | Improving Neural Networks (Learning rate, momentum, overfitting, L1/L2, dropout, batch normalization, label smoothing) |
| Week 6 | Radial Basis Function Networks (Gaussian basis functions, K-means, center selection, pseudo-inverse, gradient descent) |
| Week 7 | QUIZ 1 && Self-Organizing Maps (SOM) (Competitive learning, BMU, neighborhood, clustering, visualization) |
| Week 8 | Midterm Week |
| Week 9 | Midterm Week |
| Week 10 | Grow-When-Required (GWR) & ECoS (Incremental learning, adaptive growth, online learning) |
| Week 11 | Hopfield Networks (Associative memory, Hebbian learning, energy function, convergence) |
| Week 12 | Time-Delay & Elman Networks (Tapped delay line, teacher forcing, recurrent connections) |
| Week 13 | GRU & LSTM (Vanishing gradients, gates, sequence learning) |
| Week 14 | Convolutional Neural Networks (Convolution, filters, padding, stride, pooling, CNN architectures) |
| Week 15 | QUIZ 2 && Model Evaluation & Modern Applications (Train/validation/test split, confusion matrix, precision, recall, F1, ethics, applications) |
Reference Books & Course Materials
- 01 Haykin, S. Neural Networks and Learning Machines, 3rd Edition, Pearson, 2009.
- 02 Aggarwal, C. C. Neural Networks and Deep Learning: A Textbook, Springer, 2018.
- 03 Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning, MIT Press, 2016.
- 04 Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 3rd Edition, O'Reilly Media, 2022.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Should have sufficient knowledge in mathematics, science, and subjects specific to the relevant engineering discipline.
- P02 Should have the ability to use theoretical and applied knowledge in mathematics, science, and related engineering disciplines in complex engineering problems.
- P03 Should have the ability to detect, define, formulate, and solve complex engineering problems.
- P04 Should have the ability to select and apply appropriate analysis and modeling methods to solve complex engineering problems.
- P05 Should have the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions.
- P06 Should have the ability to apply modern design methods.
- P07 Should have the ability to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications.
- P08 Should have the ability to use information technologies effectively.
- P09 Should have the ability to design experiments, for the study of complex problems or discipline-specific research topics.
- P10 Should have the ability to conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research topics.
- P11 Should have the ability to work in intradisciplinary teams.
- P12 Should have the ability to work in interdisciplinary teams.
- P13 Should have the skills to work individually.
- P14 Should have the ability to communicate effectively verbally and in writing.
- P15 Should have the knowledge of at least one foreign language.
- P16 Should be able to write effective reports, understand written reports, and prepare design and production reports.
- P17 Should have the ability to make effective presentations.
- P18 Should have the ability to give and have clear and understandable instructions.
- P19 Should gain consciousness (awareness) about the necessity of lifelong learning.
- P20 Should have the ability to access information.
- P21 Should have the ability to follow developments in science and technology and constantly renew himself/herself.
- P22 Should gain the awareness of professional and ethical responsibility and should act in accordance with ethical principles.
- P23 Should gain knowledge about the standards used in engineering applications.
- P24 Should gain knowledge about project management, risk management, and change management practices in business life.
- P25 Should gain awareness about entrepreneurship, and innovation.
- P26 Should gain knowledge about development in sustainability.
- P27 Should gain knowledge about the effects of engineering practices on health, environment, and security at universal and social dimensions and the problems of the age reflected in the field of engineering.
- P28 Awareness should be gained about the legal consequences of engineering solutions.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.