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Enseignement de Master 2 - DK902 : Natural and Artificial Intelligence


Bringing machines closer to human competence is a fascinating challenge. We can hardly anticipate all the technical consequences that competent machines will have in domains such as human-machine interaction, intelligent search engines, machine translation, robotics, pattern recognition, knowledge mining and learning, adaptive planning or personal assistance.

This course addresses the issue of A.I. as a reverse-engineering problem: try to mimic, not only the performance, but also the processes, of natural intelligence. For example, a text-messaging app reading “The meeting is scheduled for tomorrow.” anticipates future tense: “Will [you be there]?”. It does so through mere statistical association between “tomorrow” and future tense. Could a machine detect that the message is about a future event, and then not only deduce that future tense is appropriate, but also retrieve the reason for attending the meeting?

This course is best adapted to students who want to acquire more than skills in the domain of Artificial Intelligence.

Objectifs pédagogiques

 This course will present several models of human cognition that can lead to computer implementation.
The objective is not only technical. It is an occasion to grasp the complexity and power of human intelligence, while drawing a line between capacities that can be implemented and those that remain challenging to reproduce.

21 heures en présentiel
réparties en:
  • Leçon : 14
  • Travaux pratiques : 7

Diplôme(s) concerné(s)

Pour les étudiants du diplôme Data & Knowledge (D-K)

Basic knowledge in Logic (propositional logic and predicate logic) and in logic programming.

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Data & Knowledge (D-K)

Vos modalités d'acquisition :

First session: 30% lab questions + 30% paper + 10% presentation + 30% quiz.
Second session: 50% first session + 50% oral examination

Answers to questions during lab sessions are recorded and read (30%). In addition, each student will choose a technical topic (typically a topic studied during lab sessions), perform a micro-research on that problem (typically go beyond the lab exercises) and write a 4-page paper (30%).
Students will briefly present their work on the last day (10%). Students will also answer a small quiz on the last day (no documents). Students who would fail to pass this first round will have to prove that they master the main concepts taught in the course during an oral interview. The eventual note will be the mean of the first grade and this oral evaluation.

Le rattrapage est autorisé (Note de rattrapage conservée)
    L'UE est acquise si Note finale >= 10
    • Crédits ECTS acquis : 2.5 ECTS

    La note obtenue rentre dans le calcul de votre GPA.

    Programme détaillé

    ● Symbolic machine learning
    ● Cognitive knowledge representation
    ● Introduction to Natural Language Processing (syntax, semantics, relevance)
    ● Reasoning, complexity, simplicity
    ● Emotions and computation

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