6 ECTS credits
160 h study time
Offer 1 with catalog number 4019816ENR for all students in the 1st semester at a (E) Master - advanced level.
The aim of the course is to cover deep learning algorithms, architectures and systems, and to present applications in various modern data processing and analysis tasks.
The course content is divided in the following parts.
Part I presents concepts around deep neural networks for supervised learning (regression and classification), including forward- and back-propagation, various modern gradient descent based optimization algorithms, loss functions and regularization methods. This part covers various deep neural network architectures including fully-connected neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms and Transformers. Applications of such models in computer vision, natural language processing and data mining are also discussed.
Part II presents deep learning models for unsupervised learning, including autoencoders and their regularized and denoising versions. This part also covers deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs).
Part III discusses advanced topics in deep learning, including transfer learning, learning from multimodal data, and learning from few examples.
The goal of this course is to introduce the various concepts, methods, and technologies relevant for the design of deep learning methods for modern data generation, processing and analysis. The students will have the opportunity to follow a set of lectures, implement the concepts during lab sessions in Python and practically deploy these concepts in the form of a project.
At the end of this course, the student will have developed a deep knowledge and understanding in state-of-the-art concepts and technologies in deep learning for supervised and unsupervised learning tasks. The student will be able to formulate, grasp, and analyse various deep learning models and architectures, and to address various data generation, processing and analysis tasks. The student will understand the basic coding technologies in deep learning (including Python libraries, PyTorch, etc.) and will be able to practice these tools in the form of practical sessions. The student will be able to investigate how the acquired theoretical and practical knowledge can be applied to address a practical machine learning problem in the form of a project.
This course contributes to the following programme outcomes of the Master in Applied Computer Science:
MA_A: Knowledge oriented competence
1. The Master in Engineering Sciences has in-depth knowledge and understanding of exact sciences with the specificity of their application to engineering
3. The Master in Engineering Sciences has in-depth knowledge and understanding of the advanced methods and theories to schematize and model complex problems or processes
4. The Master in Engineering Sciences can reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
6. The Master in Engineering Sciences can correctly report on research or design results in the form of a technical report or in the form of a scientific paper
8. The Master in Engineering Sciences can collaborate in a (multidisciplinary) team
11. The Master in Engineering Sciences can think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information
MA_B: Attitude
12. The Master in Engineering Sciences has a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
MA_C: Specific competence
18. The Master in Applied Computer Science is able to design and use systems for efficient storage, access and distribution of digital information
19. The Master in Applied Computer Science has knowledge of and is able to use advanced processing methods and tools for the analysis of (big) data in different application domains.
The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.
Within the Other Exam category, the following assignments need to be completed:
The final grade is composed of the following examinations: (1) the result of the final exam, which determines 70% of the final mark; (2) the attendance and code reporting for the practical sessions, which determines 10% of the final mark and (3) the result of a project work, which determines 20% of the final mark.
The final exam will be a written evaluation, where the students will address theoretical questions, will be asked to define optimization functions, algorithms and model architectures that solve specific deep learning problems. The students need to attend at least 70% of the lab sessions and deliver their code afterward. The project examines the students’ in-depth understanding of deep learning algorithms, models and systems, and assesses their practical skills in real-life deep learning tasks. The project is delivered as a report along with the associated code and a final Q&A session is organised for its assessment.
This offer is part of the following study plans:
Master of Biomedical Engineering: Standaard traject (only offered in Dutch)
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject
Master of Biomedical Engineering: Startplan
Master of Biomedical Engineering: Profile Radiation Physics
Master of Biomedical Engineering: Profile Biomechanics and Biomaterials
Master of Biomedical Engineering: Profile Sensors and Medical Devices
Master of Biomedical Engineering: Profile Neuro-Engineering
Master of Biomedical Engineering: Standaard traject (NIEUW)
Master of Biomedical Engineering: Profile Artificial intelligence and Digital Health