Study Programmes 2017-2018
WARNING : 2016-2017 version of the course specifications
Programming techniques
Duration :
30h Th, 24h Pr, 70h Proj.
Number of credits :
Bachelor in computer science5
Master in data science (120 ECTS)5
Master of science in computer science and engineering (120 ECTS)5
Master in data science and engineering (120 ECTS)5
Master in computer science (120 ECTS)5
Master in computer science (60 ECTS)6
Master in mathematics (120 ECTS)6
Lecturer :
Laurent Mathy
Language(s) of instruction :
English language
Organisation and examination :
Teaching in the second semester
Units courses prerequisite and corequisite :
Prerequisite or corequisite units are presented within each program
Learning unit contents :
The course runs as two parts: the first part addresses algorithmic problem solving and describes examples of advanced algorithms, using C as the reference programming language. The second part provides an introduction to parallel programming on multi-core systems, using Java as the reference programming language.
Specifically, the first part content comprises the following topics: programming as problem solving; advanced sorting; balanced search; radix search; external algorithms; algorithms on graphs. The second part content comprises: principles of multiprocessor programming; contention and scalable locking; introduction to parallel data structures and algorithms; scheduling and work distribution.
Learning outcomes of the learning unit :
In this course, the students learn: - the principles of complex program decomposition; - to write efficient programs - knowledge of advanced algorithmic techniques - techniques for parallel programming
Prerequisite knowledge and skills :
Knowledge of basic algorithms. Practical knowledge of the C and Java programming languages.
INFO0902 or INFO2050
Planned learning activities and teaching methods :
Lectures and practicals, both involving problem solving in class. The students carry out several assignments: some individual  programming assignments and some group mini-projects. The exact number and type of assignment varies depending on the year, although the student workload is kept similar.
Mode of delivery (face-to-face ; distance-learning) :
Weekly lectures and practicals, Q2
Recommended or required readings :
Optional recommended readings: Introduction to algorithms; Cormen, Leiserson, Rivest and Stein; MIT press. Algorithms in C; Sedgewick; Addison Wesley. The Art of Multiprocessor Programming; Herlihy and Shavit; Morgan Kaufmann.
Assessment methods and criteria :
Written and oral  exams and assignments.The assignments count towards 40% of the final mark, while each exam counts towards 30% of the final mark. Each part of the course bears equal weight in the final mark. During the written exam, the students can use the lecture and practical notes that were officially distributed on the myulg course page (and only those).
Students who do not submit at least half the projects receive an absence mark for the corresponding exam session.
There is no guaranteed support for projects to be resubmitted for the resit session.
Work placement(s) :
Organizational remarks :
Contacts :