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.

1st semester
Enrollment based on exam contract
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Taught in
Faculty of Engineering
Electronics and Informatics
Educational team
Nikolaos DELIGIANNIS (course titular)
Activities and contact hours
24 contact hours Lecture
30 contact hours Seminar, Exercises or Practicals
40 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 representation and analysis and to present its applications in the field of modern data analytics. The course content is divided in four basic parts. Part I revolves around the basics of modern data analytics, including the key ideas and application scenarios behind supervised and unsupervised learning, and a compact recap on relevant linear algebra and optimisation methods. Part II focuses on the topic of regression for predictive analytics, covering concepts related to covariance and correlation, multivariate linear and non-linear regression, ridge regression and regularisation, and matrix completion for recommender systems. Part III continues on supervised learning, focusing on classification problems including logistic regression, softmax, and neural networks (convolutional neural networks, recurrent neural networks, etc). Finally, Part IV focuses on unsupervised learning for data representation, addressing topics related to clustering (k-means, DBSCAN, and hierarchical clustering), and dimensionality reduction by means of Principle Component Analysis, Singular Vector Decomposition and auto-encoders. Furthermore, Part IV covers the topic of sparse data representations, including dictionary learning, sparse coding methods and sparsity enforcing data transformations. 

Course material
Digital course material (Required) : Data representation, reduction and analysis, Slides, Course notes and exercises [in Python], N. Deligiannis, 2017
Handbook (Recommended) : An introduction to statistical learning, James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani, Vol. 112. New York: springer, 2013, 2013
Handbook (Recommended) : Deep learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Vol. 112. New York: springer, 2017, 2017
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

Algemene competenties

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 data representation as well as in supervised and unsupervised learning tasks and in signal processing methods. The student will be able to formulate, grasp, and analyse the fundamental designs in big data analytics tasks and to address data analytics problems using the latest machine learning and signal processing tools and methodologies. The student will understand the basic coding technologies in data analytics systems – including Python libraries, tensorflow, Keras, 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 addressed a practical data analytics problem in the form of a project.

This course contributes to the following programme outcomes of the Master in Applied Computer Sciences:

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)
8. The Master in Engineering Sciences can collaborate in a (multidisciplinary) team

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 Sciences is able to design and use systems for efficient storage, access and distribution of digital information
19. The Master in Applied Computer Sciences 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:

  • Written+oral+project with a relative weight of 1 which comprises 100% 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 pseudo-code that solves specific problems related to data representation, reduction and analysis. The project will examine the students’ involvement in the seminar sessions, evaluate their in-depth understanding of data representation, reduction and analysis algorithms, 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.

Academic context

This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject