Programme Description

Computer Science: Curriculum for International Students

* Pattern recognition:

Pattern recognition aims to classify data (patterns) based on either a priori knowledge or statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. Several methods can be used, e.g. neural networks or SVM. Those methods are based on supervised learning or on unsupervised learning. Selection or extraction of features is an important stage in pattern recognition.

* Collective Artificial Intelligence:

This course aims at discovering the powerfulness of new paradigms in computer science that are inspired by self-organization theories. The first part of this project introduces a natural phenomenon like prey searching or brood sorting in real ants, etc. Then a model is built from the observation of the agent-like behavior. Last, a real-world problem is tackled thanks to the model. The goal of this project is to include these three major steps: understanding the natural collective behavior, building a model and finding new solutions for a classical computer science problem (optimization, clustering etc). * The C++ programming language:

The aim of this teaching is to make the students familiar with the C++ language, which has the advantage of being an extension of the C language and implements the principles of an object-oriented language. Henceforth it is adequate for tackling such a language for those who are familiar with the C language. During this teaching, the basic concepts of C++ will be dealt with: how to manage classes, how to declare constructors/destructors, how to manage functions etc. Next, all the mechanisms of an object-oriented language are presented: inheritance, functions and classes friendship, errors and exceptions, overloading of functions and classes, templates, polymorphism, streams etc. A particular emphasis will be put on the good practical uses in programming according to the basic standards of Software Engineering.

* UML 2: Object Oriented modeling:

Overview (how to approach Object-Oriented Modeling); Structural Elements (modeling object, classes, instances, etc.); Behavior (modeling class interactions); Inheritance (modeling base and sub classes); Advanced OO Concepts (modeling relationships, packages, etc.); Best Practices (naming conventions, commenting, etc.); Design patterns

* Intelligent systems:

This course is about building Intelligent Systems using constraint programming and heuristic search methods. The technology is applied in a wide variety of applications such as scheduling, planning and design. This course will introduce the methodology of constraint programming and exact methods such as branch and bound procedures. In addition, it will introduce heuristic methods based on constructive search and iterative improvement (local) search; Constraint satisfaction: definition of constraint satisfaction problems (CSPs); examples in intelligent systems; constraint graphs; levels of consistency; constraint propagation algorithms; Constructive Search: chronological backtracking algorithms; Local Search: greedy methods; iterative repair methods; Neighbourhoods; hybrid algorithms; Scheduling and planning applications: traditional methods; Branch & Bound procedures, flow shop, job shop, resource allocation and other applications. - Constraint Programming (CP) tools: introduction to the CLP framework; types of constraint solvers; the CHOCO platform.

* French language and culture: Initiation to the French language.