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 Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.
- P02 Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
- P03 Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
- P04 Ability to devise, select, and use modern techniques and tools needed for analysing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively
- P05 Ability to design and conduct experiments, gather data, analyse and interpret results for investigating complex engineering problems or discipline specific research questions.
- P06 Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
- P07 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
- P08 Recognition of the need for lifelong learning ; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
- P09 Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.
- P10 Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
- P11 Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.