5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Schaus Pierre;
Language
French
Prerequisites
Within SINF1BA : LSINF1101 and LSINF1103
Within FSA1BA : LFSAB1401, LFSAB1101, LFSAB1102, LFSAB1201, LFSAB1202, FSAB1301
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Within FSA1BA : LFSAB1401, LFSAB1101, LFSAB1102, LFSAB1201, LFSAB1202, FSAB1301
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
- Computational complexity
- Specifications and object-oriented design
- Basic data structures (lists, trees, binary search trees): study of their abstract properties, practical representations, concrete applications and associated algorithms
- Advanced data structures and algorithms: hash tables, heaps, balanced search trees, text processing techniques, dictionaries, graph representation and processing
Aims
At the end of this learning unit, the student is able to : | |
| 1 | Given the learning outcomes of the "Bachelor in Computer science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Given the learning outcomes of the "Bachelor in Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
Students completing successfully this course will be able to
Students will have developed skills and operational methodology. In particular, they have developed their ability to:
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Computational complexity,
- Trees, binary search trees,
- Balanced trees,
- Dictionaries and hash tables,
- Priority queues and heaps
- Graphs,
- Text processing (pattern matching, compression algorithms)
Teaching methods
The active teaching method followed in this course is based on a Problem Solving Approach. This method is based on several phases of work, some supervised by tutors. In addition to tutored sessions, an essential component of this pedagogical approach is to promote each student to learn by himself. The success of the learning process presupposes a significant involvement of each student. The role of group work is mainly to discuss the concepts studied and, secondarily, to organize the work of each. Learning itself remains the responsibility of each student.
The work is organized into missions that each group of students must perform with strict deadline (typically 2 weeks per mission). These missions include questions for which each group must make the best possible answers and programming problems, for which the group must produce Java programs.
The missions are intended primarily to create a context and motivation for learning new concepts and for developping methodological skills. Reach the end of a mission is not an end in itself. It is important to keep in mind that each mission is primarily an opportunity to develop learning goals explicit in the statement of each mission.
The work is organized into missions that each group of students must perform with strict deadline (typically 2 weeks per mission). These missions include questions for which each group must make the best possible answers and programming problems, for which the group must produce Java programs.
The missions are intended primarily to create a context and motivation for learning new concepts and for developping methodological skills. Reach the end of a mission is not an end in itself. It is important to keep in mind that each mission is primarily an opportunity to develop learning goals explicit in the statement of each mission.
Evaluation methods
A note of PARTICIPATION on different missions and programming problems (Inginious , etc. ) accounts for 15% of the final grade in January. Only the final exam is taken into consideration for the final grade of second session (the participation is no longer considered) .
Other information
Background:
- master an object-oriented programming language (p.e. Java)
- know an use correctly basic data structures (stacks, queues, lists, etc..)
- have basic knowledge of recursion and computational complexity.
Online resources
https://moodleucl.uclouvain.be/course/view.php?id=7682
Bibliography
Textbook obligatoire :
Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne, Addison-Wesley Professional.
ISBN-13: 978-0321573513
ISBN-10: 032157351X
Et plus généralement les documents (énoncés des missions, conseils pour l'examen, ...) disponibles sur : http://moodleucl.uclouvain.be/course/view.php?id=7682
Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne, Addison-Wesley Professional.
ISBN-13: 978-0321573513
ISBN-10: 032157351X
Et plus généralement les documents (énoncés des missions, conseils pour l'examen, ...) disponibles sur : http://moodleucl.uclouvain.be/course/view.php?id=7682
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Mathematical Engineering
Bachelor in Mathematics
Master [120] in data Science: Statistic
Minor in Statistics and data sciences
Minor in Engineering Sciences: Computer Sciences
Additionnal module in Statistics and data science