Introduction Scientific Computing (WISB 356), 2018/2019

Location and time

Monday (13.15-17 hour) and Thursday (9-12.45 hour) from February 4, 2018 until April 4, 2018 in room BBG115 at the Uithof campus in Utrecht. Not on: March 4, 7 (break week).


Module 1: Rob Bisseling (Mathematical Institute UU)
Module 2: Alessandro Sbrizzi (University Medical Center Utrecht)
Teaching assistent: Ruben Meijs (Mathematical Institute UU)

Course material

Module 1 (4 weeks): begin intro Matlab, then Chapter 5 (linear systems) and 7 (PageRank) from the online book by Cleve Moler (2011), Experiments with Matlab. We will use other materials as well.

Module 2 (4 weeks): Magnetic Resonance Imaging (MRI). Online material will become available soon via Alessandro Sbrizzi's ISC webpage.


We use Matlab, see Free software at the UU for students. Bring your own laptop, because we work by the principle Bring Your Own Device (BYOD). In class there are no desktop computers. In Module 2 we will use the Image Processing Toolbox, which can be downloaded with the Matlab package. Tick a box when installing.


All information on module 1 can be found on the present page. All information on module 2 can be found on Alessandro Sbrizzi's ISC webpage.


Based on two reports, one per module. Both reports have equal weight and count for 50% of the final grade of the course. Every report needs to obtain a grade of at least 5, and the rounded final grade must be at least 6. The reports can be written in either Dutch or English. The reports can be written together with (at most) one fellow student. Every student is individually accountable for the whole report. Reports can be discussed individually afterwards. This may influence the final grade.


7,5 ECTS


The aim of this course is to provide a first orientation towards the area of scientific computing by some case studies from various application areas. Topics treated are widely used techniques from numerical linear algebra such as the solution of linear systems and eigenvalue problems, both dense and sparse, within the context of an application such as computing the Google PageRank of a webpage or the processing of images obtained by an MRI scanner. We will also study algorithms of a more combinatorial nature, such as the partitioning of sparse matrices. Both theoretical aspects and practical, software-related aspects will be treated. Every week there will be frontal lectures alternating with exercise/computer laboratory classes. This course presents a taste of the new master track Applied Mathematics, Complex systems, and Scientific Computing and it represents an overview of scientific computing. The connection to practice is further established by one or more guest teachers.


Linear algebra (WISB121), Calculus A (WISB132), Calculus B (WISB137). The course Numerical Mathematics WISB251 is desired, but not strictly necessary. When in doubt, or for majors other than in Mathematics or Physics, please contact one of the teachers. It is not necessary to know Matlab already, as we will start with a gentle introduction to Matlab. Wsrning: be aware that the level of difficulty of the course will gradually increase during the period of the course, both conceptually and practically, so that near the end (in the second module) we expect the maximum effort from the student.


We roughly follow the schedule below. Ch5 means Chapter 5 from the book by Cleve Moler, "Experiments with Matlab", 2011. Small changes may still occur depending on our progress.

Module 1

Last update of this page by Rob Bisseling: March 7, 2019.