ARTIFICIAL NEURAL NETWORKS
- Course
- ISYE545 - ARTIFICIAL NEURAL NETWORKS
- Department
- Information Systems Engineering - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
Introduction to cognitive science. Parallel, distributed problems. Constraint satisfaction. Liopfield model. Supervised vs. unsupervised learning. Single vs. multi-layer perceptions. Static vs. dynamic network architecture. Comparison of neural approaches with parametric and non-parametric statistical methods. Neural network applications.
ARTIFICIAL NEURAL NETWORKS
Evaluation Tools (Active Term)
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Course outcomes
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Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction to Artificial Intelligence, Artificial Neural Networks and Deep Learning |
| Week 2 | Image Formation, Biological Neuron, Artificial Neural Networks |
| Week 3 | Artificial Neural Networks - Single Perceptron, Multi-layer ANNs, backprobagation |
| Week 4 | Convolutional Neural Networks - building blocks |
| Week 5 | Convolutional Neural Networks - applications and Phyton Coding using Tensorflow Keras |
| Week 6 | Long-Short-Term Memory Networks (LSTM) - building blocks and applications |
| Week 7 | LSTM - applications and Phyton Coding using Tensorflow Keras |
| Week 8 | Autoencoders - building blocks and applications |
| Week 9 | Transformer Networks for Natural Language |
| Week 10 | Vision Transformers for Image Recognition |
| Week 11 | Convolutional Vision Transformers |
| Week 12 | Training a Deep Learning Model - in Phyton Tensorflow Keras |
| Week 13 | Hyperparameter Analysis - optimizers, learning rate, batch size, etc. |
| Week 14 | Revision of Concepts |
| Week 15 | Finals |
Reference Books & Course Materials
- 01 Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016. https://www.deeplearningbook.org/
Learning Outcomes
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Program Outcomes
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