Cours - Intelligence artificielle et données
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Cours de intelligence artificielle et données, partagés par des étudiants et des enseignants. Thèmes couverts : machine learning, apprentissage automatique, big data, data science, fouille de données.
The document provides an in-depth overview of Big Data, its definition, importance, characteristics (5Vs: Volume, Variety, Velocity, Veracity, Value), and applications spanning industries like healthcare, marketing, politics, sports, and public security. It discusses technological frameworks like Hadoop and Spark, the methodologies for data processing, and strategies for handling limitations of traditional systems. The document emphasizes distributed systems, parallel processing, scalability, and cost-efficiency as solutions to manage and analyze extensive datasets effectively.
Ce cours vise à introduire les étudiants aux systèmes d’information décisionnels et à maîtriser leur mise en place. Il est structuré autour de quatre modules couvrant les concepts fondamentaux jusqu'à l'analyse des données. La formation se déroule à distance avec un soutien tutoriel et des séances synchrones.
This Master’s thesis focuses on designing and implementing a maintenance dashboard for CLC-Délice, using Business Intelligence (BI) principles. The methodology involves analyzing the current system, proposing improvements, and creating a dashboard to streamline maintenance management. The solution integrates multidimensional data models, ETL processes, and OLAP analysis to enhance data access and operational efficiency. The study evaluates the deployed application, showcasing significant advancements in maintenance reporting, decision-making, and cost optimization.
This document explores the development of a business intelligence solution for the Arab Tunisian Bank (ATB). The methodology integrates SCRUM, an agile framework for iterative project management, to design and implement a data warehouse and decision-support tools. Key tools include SQL Server Integration Services (SSIS) for ETL processes, alongside comparative evaluation of ROLAP, MOLAP, and HOLAP. The solution emphasizes risk classification and reporting through dynamic dashboards, enhancing ATB’s strategic decision-making capabilities.
This document outlines the conceptualization and development of the Magpie Business Intelligence platform. It begins with fundamental Business Intelligence concepts, comparing multiple open-source tools for Extract, Transform, Load (ETL), data warehouses, and reporting generators to select the most efficient components. The design phase focuses on user roles and the system's dashboard construction, emphasizing data indicators and information collection. The implementation leverages environments such as SQL Server, MySQL, Eclipse Indigo, and Tomcat to develop a robust and scalable platform,...
This document presents the development of a decision-support system for the Arab Tunisian Bank (ATB), as part of an undergraduate applied Informatics degree. The project utilizes the SCRUM methodology to structure the creation of a data warehouse, establish an ETL pipeline, and implement tools for data analysis and reporting. The project emphasizes integrating predictive and agile methodologies, comparing Business Intelligence solutions, and designing a risk classification DataMart to facilitate informed decision-making. Key outcomes include the successful deployment of a comprehensive BI p...
This document provides an introductory course on SQL Server Integration Services (SSIS) within the realm of Business Intelligence (BI). It explains ETL (Extract, Transform, Load) processes as a data-centric methodology, covering data extraction, transformation, and loading into decision-support storage structures. The course introduces SQL Server 2008 Integration Services tools and environments, including the Business Intelligence Development Studio, emphasizing features like control flow, data flow, and connection management. It aims to equip readers for practical SSIS use and certificatio...
This document is a collaboration by first-year Master’s students at the National School of Computer Sciences, supervised by Professor Faouzi Ghorbel. The work summarizes courses on computer vision, 3D techniques, stereoscopic calibration, geometric moments for shape recognition, the expectation-maximization algorithm, and dimensionality reduction using Fisher's method. The methodologies cover camera calibration, mathematical principles in image geometric transformations, algorithms for pattern recognition, and statistical approaches to data dimensionality reduction. The document provides th...
The document introduces the fundamental concepts of 2D and 3D image representation and processing. It distinguishes between low-level image processing (e.g., noise reduction and contrast enhancement) and high-level image analysis (e.g., information extraction). Key methodologies for 3D image representation, including explicit, implicit, parametric, and polygonal models, are discussed, as well as their applications in fields like archeology, medicine, and biometrics. Finally, the limitations of 2D imaging, such as pose and illumination issues, are addressed, with solutions provided by 3D ima...
This document outlines the syllabus for the TERI (Traitement et Reconnaissance d'Images) course, aimed at providing a foundational understanding of image processing and pattern recognition. Core topics include discrete image representation, filtering, segmentation, and recognition, while integrating advanced topics such as artificial intelligence and visual perception mechanisms. Practical sessions include machine labs and hands-on exercises to reinforce theoretical concepts. The syllabus also covers industrial applications and advanced mathematical models like convolution, Fourier transfor...
The document focuses on the graphical capabilities of R, explaining how to organize and structure multiple figures within a single graphical window. It introduces the concepts of managing margins (internal and external), data zones, and provides detailed instructions on using functions such as par(), split.screen(), and layout() for arranging graphic elements dynamically. Practical examples illustrate various configurations and modular splitting techniques that enhance visual presentations in R.
This document provides an introduction to the MDX language which is used for OLAP operations on multidimensional databases. It covers the basic syntax, members, tuples, and advanced expressions in MDX. The document is intended for students and professionals looking to understand and utilize MDX in data warehousing.
Ce chapitre présente les concepts fondamentaux des systèmes interactifs d’aide à la décision (SIAD) et leur impact sur le processus décisionnel. Il souligne l'importance de l'aide à la décision par rapport à la prise de décision automatique et discute des caractéristiques et des avantages des SIAD. En outre, il aborde l'évolution des SIAD et leur utilisation dans des contextes de décision complexes.
This course introduces the concepts of Business Intelligence (BI) and its relevance in data analysis for businesses. It covers data extraction, processing, and the use of BI tools while providing insights into real-world applications. The course also includes evaluations through homework and a final exam.
The document introduces the concept of data warehouses as specialized data storage systems designed to support strategic decision-making through integrated, subject-oriented, non-volatile, and dated data. It outlines core methodologies like ETL processes for extracting, transforming, and loading data, as well as the architectural design zones such as staging areas, storage zones, and presentation layers. The roles of data marts as subsets serving specific business functions are discussed, alongside advanced modeling techniques, including dimensional models like star and snowflake schemas. P...
The document elucidates the concept and architecture of data warehouses (DW), emphasizing their utility for strategic and tactical decision-making by non-technical stakeholders. It provides insight into the fundamental features of DW, including non-volatility, integration, time orientation, and subject orientation, complemented by detailed discussions on ETL (Extract, Transform, Load) processes and data modeling techniques such as star and snowflake schemas. Additionally, it explores practical applications of DW across industries like finance, healthcare, and logistics and briefly compares...
This document explores expert systems as a subset of artificial intelligence, aiming to replicate human expert reasoning through a knowledge base, rule base, and inference engine. Techniques for deduction include forward chaining, backward chaining, and mixed chaining. It discusses tools such as NEXPERT OBJECT, CLIPS, and Prolog, highlighting their strengths, paradigms, and integration capabilities. The application of expert systems spans domains like medicine (diagnostics), aerospace (traffic planning), and IT (programming assistance).
This course provides an understanding of data warehousing concepts and the management of data systems. It emphasizes the importance of mastering the fundamental notions related to data warehousing and the ability to model and design a data warehouse. Students will learn to manipulate tools used for data warehousing through practical applications.
Ce document traite de la détection de points d’intérêts dans les images, un processus essentiel dans la vision par ordinateur. Les méthodes de détection sont classées en trois catégories : basées sur les contours, sur l'intensité, et des approches basées sur des modèles. Le détecteur de Moravec est également présenté comme une méthode pour identifier les changements d'intensité dans une zone considérée.
Ce document traite des différentes catégories de raisonnement en intelligence artificielle, notamment le raisonnement déductif, inductif et abductif. Il aborde également la question de l'incertitude et de l'approximativité dans les raisonnements, ainsi que l'importance du temps dans les systèmes d'IA. Enfin, il explore diverses logiques et méthodes pour traiter ces aspects dans le domaine de l'IA.
Ce document explore les notions du raisonnement en intelligence artificielle, en se concentrant sur les symboles et la représentation des connaissances. Il aborde l'historique et les définitions des ressources nécessaires pour un raisonnement efficace. Différents types de raisonnement, ainsi que leurs techniques et défis, sont également discutés.
This document discusses classification methods in data mining, focusing on Naive Bayes with numeric attributes and examples regarding probability calculations. It also addresses the limitations of Naive Bayes with nominal attributes and introduces decision tree algorithms such as ID3, C4.5, and C5.0 for numeric attributes.
This document covers a course on Data Mining focusing on Decision Trees. It includes definitions, examples, and how to construct a decision tree. Key methods of classification such as ID3, C4.5, and C5.0 are also discussed.
This document provides an overview of supervised classification using decision trees. It includes an introduction to the structure of decision trees and details the ID3 algorithm along with its characteristics. Additionally, the document references course notes and literature related to the topic.