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Artificial Intelligence Laboratory LIA


Old Courses

Algorithmic Graph Theory and its Applications

We present classical and more recent theory and algorithms in graph theory, in particular, related to intersection graphs and other related families of graphs. Intersection graphs are an extremely useful mathematical model for many applications in computer science, operations research, and even molecular biology. We begin by explaining and motivating the concept, and provide examples from the literature, following the textbook for the course and other references. As a particular instance of the interplay between the mathematical theory and algorithm design, the graph coloring problem on special families of these intersection graphs will be studied. Many of these techniques can be applied to scheduling classrooms or airplanes, allocating machines or personnel to jobs, or designing circuits. Rich mathematical problems also arise in the study of intersection graphs. We will present a spectrum research results, from simple to sophisticated. This course is given in English. You can find more detailed information on the organization of the course here.

Natural Language Processing / Computational Linguistics

(Graduate course in predoctoral school)

The principal objective of this course is to present the different models, formalisms and algorithms that can be used for an efficient development of natural language parsers (i.e. syntactic analyzers) in the framework of industrial developments. This course is given in English, find more detailed information on the organization of the course here.


(Mandatory for computer science students) Topics treated in this course include mutual information and entropy as well as theory of coding, compression, error correction and cryptography. This course is given in French. You can find more detailed information on this course here. find more detailed information on the organization of  the course here(password protected, students only).

Distributed Information Processing

(Graduate course) In the context of distributed information processing fields of information systems and artificial intelligence have a substantial potential for converging. Fundamental common problem: autonomy of distributed (information processing agents). Distributed information systems have to deal with the consequences of autonomy: heterogeneity, inconsistency, inefficiency. Artificial Intelligence has developed in the area of agents methods to deal with autonomy, planning, coordination, negotiation. In order to successfully take advantage of the convergence of the areas one needs to know the fundamentals of both of them. This course is given in English. Find more detailed information on the organization of  the course here.

Knowledge Systems (NDIT/FPIT postgraduate course)

Topics of this course include inference engines, search algorithms and constraint satisfaction, scheduling, management of innovative projects, case-based reasoning, diagnosis with logic, probabilities and models. Find more detailed information on the organization of  the course here.

Language And Speech Processing (Postgraduate Course)

Given in 2001 and 2002.

This course provides the theoretical and practical background required to obtain the qualification for efficient team work in modern language and speech engineering, it includes lectures on speech signal processing, speech and speaker recognition, and natural language processing, introductions to language engineering applications, and linguistic training for language and speech engineers. This course is given in English, find more detailed information on the organization of the course here

Decision Making under Uncertainty

(Optional course for students in computer science)

This course gives a firm foundation to decision theory from mainly a statistical and algorithmic, but also a philosophical perspective. The aims of the course are two-fold. Firstly, to give a thorough understanding of statistical decision theory, the meaning of hypothesis testing, automatic methods for designing and interpreting experiments and the relation of statistical decision making to human decision making. Secondly to relate the theory to recent developments and practical problems in reinforcement learning and artificial intelligence. The course is in English.
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