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 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
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