Intelligence artificielle et données
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Cours, examens, TD, TP et exercices de intelligence artificielle et données. Thèmes couverts : machine learning, apprentissage automatique, big data, data science, fouille de données.
This document outlines an examination for a module on Decision Support Systems at the Université Virtuelle de Tunis. It includes reflective questions and a case study focusing on the implementation of a Data Warehouse for the Tunisian Post, emphasizing the monitoring of payments through various services. The examination assesses students' understanding of specific concepts and their ability to model information systems.
This document explores methodologies for analyzing retail and insurance data using data warehousing techniques. Retail data analysis focuses on sales performance across dimensions such as product, location, temporal trends, and supplier, with proposed star and snowflake schemas for structuring data. Insurance data analysis delves into policy management and claims tracking, including key metrics such as premiums, transactions, profits, and claims payments. Hierarchical data aggregation strategies and dimensional modeling drive efficiency in querying and reporting, with detailed diagrams and...
This document details a set of exercises aimed at using Apache Spark's RDD functionalities for processing large datasets. The tasks include loading datasets, filtering data based on specific criteria, working with partitions, performing transformations using map and reduceByKey, counting word occurrences in multiple files, and combining results through joins. The methodology emphasizes practical implementation of Spark's RDD operations for handling logs, purchase data, and textual analysis while utilizing caching for optimization.
This document analyzes the strategic deployment involving big data and organizational change, specifically in the automotive manufacturing sector. It discusses the implications of strategic change on organizational efficiency. Two primary applications are highlighted, showcasing the interrelation between big data and effective organizational practices.
This module addresses the growing importance of high-performance computing (HPC) in research and industry through parallel systems. It provides an overview of hardware architectures, delves into parallel algorithmic and programming methodologies, and emphasizes techniques for analyzing and designing efficient parallel algorithms. Special attention is given to automatic parallelization of polyhedral programs, utilizing tools like OpenMP and MPI. Key topics include task scheduling in homogeneous and heterogeneous environments.
This document introduces the NoSQL database paradigm in the context of distributed systems and Big Data requirements. It explores scalability issues, highlighting horizontal versus vertical scaling, and explains distributed frameworks like MapReduce for processing large data sets. The CAP theorem is discussed in the context of trade-offs between consistency, availability, and partition tolerance. Additionally, types of NoSQL databases such as key-value stores, column-oriented databases, graph databases, and document-based databases are detailed, providing insights into their strengths, weak...
This document details a Data Warehousing exam administered by the Université Virtuelle de Tunis. It covers two exercises: (1) designing a star schema and a snowflake schema to improve sales data collection and analysis for a chain of magazine stores, with specific analytical indicators and SQL queries for sales analysis; (2) creating star and snowflake schemas for an academic institution to understand factors affecting student exam success, including demographic and course-specific performance data. The document emphasizes the importance of dimensional modeling and schema transformations fo...
This document outlines an exam for the Master Professional in Business Intelligence at the Université Virtuelle de Tunis. It consists of exercises focusing on data modeling and integration for a chain of stores specializing in newspapers and magazines. The exam assesses the ability to create star and snowflake schemas, along with SQL query formulation and analysis of sales data.
This document presents exercises for a Master's level course on data warehousing. It includes tasks on designing data marts to analyze sales data in various contexts, such as disposable dish manufacturing and fast-food restaurants. The exercises also involve creating models in star and snowflake schemas.
This document focuses on the definition, architecture, and methodologies of Data Warehouses (DW). It explains key concepts such as subject-oriented, integrated, non-volatile, and historical data storage principles (Inmon's definition). Additionally, it outlines dimensional modeling techniques like star and snowflake schemas, emphasizing their role in decision-making processes. Architectural approaches by Inmon (Corporate Information Factory) and Kimball (Dimensional Data Warehouse) are compared, highlighting their core differences in data storage and access strategies. Practical steps for i...
This module covers the essentials of Data Warehousing, focusing on the implementation process and modeling concepts. It highlights the importance of data and analysis in decision-making for modern businesses. By the end of the course, participants will be able to explain the objectives of a Data Warehouse and effectively distinguish it from transactional databases.
This document explores the application of fuzzy logic in two main scenarios: quality control in manufacturing and temperature regulation of buildings. The first exercise involves constructing a fuzzy logic-based decision-making system for smartphone quality based on weight and length, utilizing trapezoidal and triangular membership functions. The second exercise designs a temperature regulation system for buildings using fuzzy controllers, with inputs from internal and external temperature sensors, and varying power outputs determined by predefined linguistic terms and membership functions....
The document presents exercises focusing on fuzzy logic inference methods within AI foundations. It progresses from calculating membership degrees of temperature variables to graphical representations of fuzzy functions, explores inference rules via Mamdani's Max-Min method, and concludes with an aggregation of rules using fuzzy control systems defined by linguistic variables. The examples emphasize practical applications like temperature regulation and control systems with multiple inputs and outputs.
Ce document contient des exercices sur l'inférence floue dans le cadre d'une matière d'intelligence artificielle. L'exercice porte sur l'application de la méthode Max-Min et l'évaluation des températures à l'aide de règles d'inférences. Il inclut également des représentations graphiques pour illustrer les fonctions d'appartenance.
This document outlines coursework focused on fuzzy logic, specifically applied to quality control in manufacturing and temperature regulation in buildings. It includes exercises that require the use of fuzzy sets and decision-making processes. Students are tasked with graphical representation of fuzzy subsets and formalization of membership functions.
This document pertains to coursework for fourth-year students enrolled in data science and INFINI at École Supérieure Privée d’Ingénierie et de Technologies during the academic year 2020-2021. It outlines an exercise labeled '1,' suggesting practical engagement or problem-solving as part of the curriculum. The methodology likely centers around applied learning and computational techniques pertinent to data science. Findings or outcomes are implied to be results of completing assigned exercises.
This document presents an analysis of six brands of orange juice evaluated by a panel of experts based on sensory variables. The evaluation includes various characteristics such as odor intensity, taste intensity, and sweetness. Additionally, a principal component analysis is performed to explore the relationships among the juices.
D ISCOURS DE SOUTENANCE Madame la pr sidente du jury, madame et messieurs les membres du jury, chers coll gues, chers amis, Remerciements Avant de me lancer dans le vif de la pr sentation de mon travail de doctorat, je souhaiterais remercier les membres du jury d avoir port int r t mon tude et d avoir accept de participer cette soutenance.
M moire de Mast re Pour obtenir le mast re en nouvelles technologies de t l communication et r seaux Th me : Conception et d veloppement d un site web de e- commerce pour le compte de LSAT_Nokia R alis par : Adel RAISSI Encadr par : UVT : LSAT_Nokia : Melle Maroua CHAABANI M.
1- ADO. NET (Activex Database Object)(1) Pr sentation Initialement chaque type de gestionnaire de base de donn es avait ses instructions, sa mani re de fonctionner ADO.
Comment les nombres n gatifs sont-ils repr sent s ? Quel est le plus grand nombre qui puisse tre repr sent par un mot machine ? Que se passe-t-il si une op ration g n re un nombre plus grand que ce qu'il n'est possible de repr senter ?
Ce document explique les principes de classification hiérarchique et non hiérarchique, en détaillant les méthodes de calcul des distances et les processus de regroupement des individus. Il aborde également les différences entre méthodes ascendantes et descendantes de classification, ainsi que les implications de l'évaluation de dissimilarité entre groupes et individus.
This document aims to explain the role of hidden layer neurons in perceptrons, using artificially generated datasets with two explanatory variables for analysis and visualization. The methodology includes constructing and evaluating multilayer perceptrons with the R 'nnet' package. Additionally, an automatic procedure for determining the optimal number of hidden layer neurons is demonstrated, leveraging training and test data splits. The findings showcase the perceptron's effective supervised learning capabilities, particularly its ability to approximate complex functional relationships bet...
This document explains the role of hidden layer neurons in a perceptron using artificially generated data. It describes the dataset used, along with the defined decision boundaries for two classification problems. Finally, it outlines the process of implementing a multi-layer perceptron with the R package 'nnet' to determine the optimal number of hidden neurons.