Knowledge Representation and Reasoning
Welcome to this course !!!
This course is taught in English. It is delivered in four chapters. The lectures material, tutorial sheets and lab sheets are written in English, however the explanations, discussions and student questions during the sessions are possible in three languages: Arabic, English and French.
Abstract:
The artificial intelligence solves the problem using two main approaches. Historically, the first used approach is the knowledge-base solution and the second one is the data-driven solution. In the former approach, the solution is developed upon the representation of expert knowledge for a specific application domain in a specific formalism. We mean by formalism a formal representation language.
Syllabus
Chapter 1: Introduction to knowledge representation and reasoning
Chapter 2: Non-classical logics
Chapter 3: Uncertain reasoning
Chapter 4: Theory of fuzzy sets
Machine Learning
This course provides an introduction to the fundamental concepts and techniques of Machine Learning. Students will explore supervised and unsupervised learning methods, model evaluation, and practical applications of machine learning algorithms to real-world data. Emphasis is placed on both the theoretical foundations and the implementation of models using modern tools and frameworks.
General Objectives
At the end of this course, the student should be able to:
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Understand the key principles and algorithms of machine learning.
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Distinguish between supervised, unsupervised, and reinforcement learning approaches.
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Apply appropriate algorithms to solve classification, regression, and clustering problems.
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Evaluate and interpret model performance using standard metrics.
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Implement machine learning solutions using programming tools such as Python and relevant libraries.
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Critically analyze the strengths, limitations, and ethical implications of machine learning systems
Business Intelligence
La Business Intelligence (BI), aussi appelée informatique décisionnelle en français, est un ensemble de technologies, d'outils et de processus qui permettent aux entreprises de collecter, organiser et analyser des données pour aider à la prise de décision. Son objectif est de transformer des données brutes en informations exploitables, présentées sous forme de tableaux de bord et de rapports, pour donner aux décideurs une vue d'ensemble de l'activité de l'entreprise afin d'en améliorer les performances.