Course: Neural Networks & Deep Learning credits: 5
- Course code
- SEVM26NNDL
- Name
- Neural Networks & Deep Learning
- Study year
- 2026-2027
- ECTS credits
- 5
- Language
- English
- Coordinator
- S.S. Ahmed
- Modes of delivery
-
- Lecture
- Project-based learning
- Assessments
-
- Neural Networks & Deep Learning - Assignment
Learning outcomes
At the end of this module the student:
- utilizes the fundamental principles of neural networks and deep learning, including architectural components, training procedures, and objective functions to implement, train, and evaluate neural network models using a modern deep learning framework such as PyTorch.
- designs and justifies neural network architectures appropriate to a given problem, considering data characteristics and task requirements.
- assesses and improves model performance using appropriate evaluation metrics, hyperparameter tuning, and systematic troubleshooting.
- applies deep learning techniques to different data modalities, such as structured, image-based, or sequential data, using architectures such as convolutional or recurrent networks.
- designs and executes an end-to-end deep learning project, including data preparation, model development, validation, and communication of results, for tasks such as classification, prediction, or anomaly detection.
Content
The module covers the following topics:
neural network fundamentals, deep learning workflows & implementation, model architectures (e.g. feedforward, convolutional, recurrent), training and optimization strategies, hyperparameter tuning, evaluation metrics and performance analysis, feature and representation learning, and project-based application to tasks such as classification, prediction, or anomaly detection.
neural network fundamentals, deep learning workflows & implementation, model architectures (e.g. feedforward, convolutional, recurrent), training and optimization strategies, hyperparameter tuning, evaluation metrics and performance analysis, feature and representation learning, and project-based application to tasks such as classification, prediction, or anomaly detection.
Included in programme(s)
School(s)
- Engineering, Life Sciences & ICT