5 ECTS credits
125 h study time

Offer 1 with catalog number 4021320ENR for all students in the 2nd semester at a (E) Master - advanced level.

2nd semester
Enrollment based on exam contract
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Taught in
Faculteit Ingenieurswetenschappen
Electronics and Informatics
Educational team
Adrian Munteanu
Nikolaos Deligiannis (course titular)
Activities and contact hours
24 contact hours Lecture
18 contact hours Seminar, Exercises or Practicals
30 contact hours Independent or External Form of Study
Course Content

The aim of the course is to cover in-depth the latest topics in data science and engineering and to present its applications in the field of big data analytics. As such, the course covers on the one hand the basic and modern principles and trends in machine learning and on the other hand the latest advances in big data systems. The course content is divided in four parts. Part I serves as an introduction to the basics of modern data analytics, including a compact recap on optimisation methods, and provides an introduction to big data systems and the cloud. Part II covers concepts related to multivariate linear and non-linear regression as well as classification problems including logistic regression, softmax. Part II focuses on unsupervised learning, addressing topics related to sparse coding and clustering. Part III focuses on deep learning with emphasis on deep convolutional neural networks for images, recurrent neural networks for time-series data, auto-encoders for data recovery and representation learning, etc. Finally, Part IV revolves around MapReduce for big data processing.

Course material
Digital course material (Required) : Course notes and Selected papers
Handbook (Recommended) : Pattern Recognition and Machine Learning, Data Mining, Inference, and Prediction, Christopher M. Bishop, 2de, Springer, 9780387848570, 2006
Handbook (Recommended) : Deep learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, New York: springer, 9780262035613, 2017
Handbook (Recommended) : Distributed systems: Concepts and Design, Concepts and Design, Coulouris J., Dollimore J., Kindberg T., Blair G., Fifth edition, Addison-Wesley, 9780273760597, 2011
Additional info

The goal of this course is to introduce the fundamental concepts, methods, and technologies relevant for the design of data analytics methods with emphasis on the latest big data analytics applications. The students will have the opportunity to follow a set of lectures, to implement the concepts during lab sessions in Python and to practically use these concepts in the form of a project.

Learning Outcomes

Learning outcomes

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 machine learning and big data analytics. The student will understand the basic coding technologies in data analytics systems – including Python libraries, tensorflow, 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 data analytics problem in the form of a project.


The final grade is composed based on the following categories:
Other determines 100% of the final mark.

Within the Other category, the following assignments need to be completed:

  • written exam with a relative weight of 7 which comprises 70% of the final mark.
  • project report + python code with a relative weight of 3 which comprises 30% of the final mark.

Additional info regarding evaluation

The final exam will be a written evaluation, where the students will address theoretical questions, will be asked to define optimisation functions and problems, as well as to write algorithms that solve specific problems related to machine learning and big data processing. The project will examine the students’ involvement in the seminar sessions, evaluate their in-depth understanding, and assess their practical skills in real-life data analytics tasks.

The final grade is composed based on the following examinations: (1) the result of the final exam, which determines 70% of the final mark; and (2) the result of a project work, which determines 30% of the final mark.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Electronics and Information Technology Engineering: Standaard traject (only offered in Dutch)
Master of Photonics Engineering: On campus traject
Master of Photonics Engineering: Online/Digital traject
Master of Electrical Engineering: Standaard traject BRUFACE J