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 overview of Big Data, explaining its core characteristics (4Vs: Volume, Velocity, Variety, Veracity) and discussing its applications and challenges. It introduces Hadoop as an open-source framework optimized for distributed computing, enabling storage and processing of massive datasets. Key technologies such as MapReduce, HDFS, and Hadoop's ecosystem are detailed, emphasizing cost-efficiency and scalability. Challenges like system heterogeneity, fault tolerance, and concurrency in distributed computing are identified, with potential solutions outlined.
This document provides an in-depth analysis of Apache Spark, a unified analytics engine for big data processing. Spark overcomes limitations of MapReduce such as high I/O latency and inefficiency in multi-pass computations. With its in-memory data processing and lazy evaluation, it achieves up to 100x faster performance in memory and up to 10x on disk compared to Hadoop. Spark allows for batch and streaming data processing, supports RDDs, DataFrames, and Datasets, and includes additional libraries for Machine Learning, graph processing, and SQL queries. Its ecosystem is expansive, with mult...
This document introduces MapReduce as a paradigm for distributed computing, focusing on its practical advantages over traditional methods for processing large datasets. It explains the methodology through key concepts such as the map and reduce operations, the division of tasks into smaller fragments, and their parallel execution across machines in a distributed cluster. Several concrete examples, such as word frequency analysis and shared-friend calculation in social networks, illustrate the practical utility of MapReduce. These examples, coupled with systematic pseudo-codes, demonstrate h...
Hadoop is an open-source framework, developed by Doug Cutting in 2004 under Apache, for distributed processing of massive datasets (petabyte scale) using clusters of commodity hardware. It facilitates distributed storage (HDFS) and computation (MapReduce) while being fault-tolerant, scalable, economical, and performance-efficient. Its ecosystem includes tools like Hive for SQL-like querying, Pig for scripting, and Mahout for machine learning. Hadoop ensures data reliability with HDFS by partitioning data into large blocks, replicating them across nodes, and utilizing NameNodes for metadata...
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.
This document outlines the structure and objectives of the first-year Master’s course in Business Analytics & Data Science, focusing on Decision Support Information Systems. It provides a 14-week program divided into four modules covering key concepts, data modeling, integration, and analytical processing using OLAP cubes. The course includes distance learning complemented by forums for discussion and tutor support via synchronous and asynchronous methods. Deadlines are enforced, and tutor assistance is available with a 24-hour response time to facilitate learning progression.
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 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...
This course introduces the fundamentals of data and text mining. It covers the methodologies for extracting high-quality information from large datasets and emphasizes the distinction between data and knowledge. Students will learn about the applications of data mining in various fields and practical exercises for analyzing data.
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 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.
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.
This document covers supervised classification methods focusing on representative examples, particularly the k-Nearest Neighbors (kNN) algorithm. It explores the concept of dissimilarity measures and their importance in classification tasks. Practical applications using Weka and reference materials are provided for further study.
This document contains notes from a data mining course focused on supervised classification methods. It includes a discussion of methods like OneR and Naive Bayes, their applications, and exercises involving datasets. Key concepts such as probability estimation in classification are also addressed.
This document presents notes from a course on Data Mining, focusing on supervised classification methods. It covers the basics of classification, including the ZeroR and Naïve Bayes methods. The aim is to explain the process of constructing classifiers and evaluating their performance.
This lecture introduces text mining and Natural Language Processing (NLP), emphasizing their definitions and applications. It addresses the challenges of analyzing natural text data and describes various types of linguistic knowledge essential for processing text. Applications such as language identification, sentiment analysis, and document categorization are discussed.
The document explains the process of classification using decision trees, starting from traversing nodes to reach a decision at leaves. It details the ID3 algorithm, emphasizing entropy and information gain for optimal attribute selection at each node. Practical examples illustrate the construction of decision trees, including scenarios of choosing attributes like age, income, and gender, and summarizing classification pathways. Additionally, the limitations of the greedy nature of ID3 and extensions to regression trees using variance minimization are discussed.