Education

The ABS group recommends a tailored set of educational courses that form the big data acceleration profile within the Computer and Embedded Systems Engineering masters program. These courses allow students to build their skills in the field and provide them with the tools needed to contribute at the bleeding-edge of research both industrially and academically. Students following these courses are valued by the industry and have access to positions in various high-tech companies such as Philips, ASML, Coolblue, etc. These courses are as follows. 

  • Introductory courses
    • CESE4010 Advanced Computing Systems (Q1): This course discusses the most widely used computational platforms (CPUs, GPUs and FPGAs), while addressing the theoretical and practical trade-offs in computer system organization and the latest developments and trends in computer architecture.
    • CESE4085 Modern Computer Architectures (Q2): This course goes in depth into the theoretical aspects of CPU architecture. The course discusses the organization of the newest microprocessors currently on the market, and the latest developments in computer architecture research.
  • HW design courses (FPGA and ASICs)
    • CESE4090 Reconfigurable Computing Design (Q2): This course introduces the students to the field of reconfigurable computing. The course provides a balanced insight of both theoretical trends and practical hands-on experience with reconfigurable technology.
    • EE4610 Digital IC Design (Q2): This course gives an overview of digital VLSI design, spanning both circuit and system abstractions. This helps students to make the right tradeoffs, find the most suitable optimizations and the best implementation strategies for VLSI circuits in standard deep-submicron CMOS technologies.
  • Machine learning courses
    • CS4240 Deep Learning (Q3): This course looks at the specific field of deep learning as part of the broader field of artificial intelligence and machine learning. Deep learning has shown remarkable modeling and predictive successes using big data sets and unstructured input data such as raw images, audio and text.
  • Big data courses
    • CESE4075 Supercomputing for Big Data (Q5): This course introduces the students to the most important concepts of big data analytics and the available tools and systems used to process it. Students will learn how to implement such pipelines and optimize them in a cloud environment.
    • IN4331 Web-scale Data Management (Q4): This course addresses the challenges of data management at web-scale. Especially, it covers the need for large-scale distributed data storage systems. It introduces an increasingly complex distributed storage systems, leading up to modern implementations of different NoSQL data storage systems.

In addition, the ABS group contributes to several masters programs, such as the Electrical Engineering and the Computer Science masters program. In particular, ABS group staff educates the Advanced Computing Systems (CESE4010) course in the first quarter as well as the Supercomputing for Big Data (CESE4075) course in the fifth quarter.