Intelligence artificielle et données
488 documents à télécharger gratuitement
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 the competition for entrance into engineering training cycles in Tunisia. It includes details about the mathematics and physics examination, along with a focus on a computer science problem related to supervised machine learning. The exam consists of problems designed to test the implementation of binary tree structures for predictive modeling.
This document highlights Python's suitability for AI and Machine Learning projects due to its simplicity, flexibility, and robust ecosystem of libraries and frameworks. Python enables developers to focus on solving ML problems rather than dealing with language complexities. It supports platform independence, efficient collaboration, and cost-effective model training. With a strong community and tools like TensorFlow, Scikit-learn, and NumPy, Python leads as the go-to programming language for AI-driven applications.
This document introduces the implementation and performance evaluation of multi-layer perceptron classifiers using Scikit-learn. It provides guidelines for classifying the Iris flower dataset, focusing on configuring MLP architectures for optimal results. The report is inspired by resources from the Scikit-learn and Python official sites. It emphasizes deadlines and highlights external references for further support.
The document explores the principles of designing and implementing an effective incremental ETL process for data warehouses. It highlights common challenges, including handling inserts, updates, and deletions in source systems, defining load order strategies, and managing dimension and fact records for historical consistency. Three ETL architectures—single-stage, two-stage, and three-stage—are introduced, alongside considerations for extraction windows and transformation planning. Finally, the text delves into efficiency measures like staging, transformation locations, and balancing extract...
This module covers the implementation of a data extraction solution focused on incremental ETL processes for data warehousing. It emphasizes planning considerations, common architectures, and data load cycles. By the end, learners will be equipped to design efficient data warehouse refresh solutions.
The document focuses on techniques for extracting modified data as part of an incremental ETL process used in data warehousing. It presents multiple methodologies such as using DateTime columns, Change Data Capture (CDC), and Change Tracking offered by SQL Server while highlighting their advantages, limitations, and use cases. Specific steps for implementing these solutions, including configuring SQL Server features, retrieving modified records, and handling deletions, are detailed. Additionally, it explains the integration of these features into ETL workflows using SQL Server Integration S...
The document provides a detailed guide for designing and implementing an ETL (Extract, Transform, Load) process to populate a data warehouse with production database data. It outlines the methodological steps including architecture selection, data extraction planning, transformation positioning and analysis, and configuring the data warehouse loading strategy. The document also references a resource for additional guidance and specifies deliverables in the form of an updated PowerPoint presentation.
This document introduces the concept of linear separation and its applications in binary classification problems. It defines the decision function g(x | w, b) and describes the conditions for classifying data points into binary categories. Multi-class classification is briefly referenced, and mathematical examples such as support vector machines (SVM) and the Iris dataset are mentioned for practical illustration. The notes emphasize the role of weights and biases in determining decision boundaries.
The document describes the applicant's passion for advancing the field of computer science and robotics by overcoming industry challenges and contributing to research and development. It highlights their recent graduation from ISTIC with a master's degree in computer science and an emphasis on data science and smart services. The applicant emphasizes their commitment to lifelong learning, knowledge sharing, and achieving educational and professional goals through research and practical application. The document culminates with their aspiration to leverage the M6P/EPFL EXAF 100 program to en...
The document outlines the methodology for structuring and designing data warehouses through source data analysis, employing techniques like star or snowflake schema modeling. Logical data schema are applied for efficient storage and retrieval. The work ensures optimized data organization, critical for reporting and decision-making processes. The project execution describes strategies for handling complex data relationships and transformations.
This document outlines the steps for designing and implementing a Data Warehouse tailored to decision-makers' needs for data visualization. Key Performance Indicators (KPIs) relevant to the business, such as sales targets, customer retention rates, and production efficiency, are identified and their calculation methods are provided. The feasibility of retrieving these KPIs from existing production databases is to be assessed, and a comprehensive data dictionary for the source databases is expected. A star or constellation schema modeling approach is proposed, followed by implementation usin...
This document provides a detailed methodology for the design and implementation of a data warehouse based on the decision-makers' needs regarding data visualization. It instructs on identifying key performance indicators (KPIs), analyzing their feasibility with existing data sources, and creating a data dictionary. It further includes the steps for star or constellation schema modeling and ends with an SQL Server implementation of the data warehouse. The document emphasizes practical application and a structured approach to ensure database alignment with organizational goals.
This document provides detailed steps for designing and implementing a Data Warehouse based on decision-maker needs for data visualization. It begins with identifying and calculating key performance indicators such as customer retention rates, productivity metrics, and absenteeism rates. It emphasizes assessing the feasibility of gathering these KPIs from source databases, creating a data dictionary for these sources, and modeling a star or constellation schema for efficient data organization. The activity concludes with the implementation of the Data Warehouse in SQL Server for real-world...
This document outlines an activity for the design and implementation of a data warehouse based on decision-maker needs for data visualization. It guides students through identifying key performance indicators (KPIs), assessing data source feasibility, creating a data dictionary, and proposing a star/constellation modeling for the data warehouse. Implementation should be done using SQL Server.
This document analyzes the performance of athletes in two sports events using multivariate data analysis. It includes 10 variables describing each athlete and focuses on deriving principal components. The eigenvalue table outlines the variance explained by each dimension, with detailed correlation and contribution values for the variables. Further analysis involves interpreting the factor map, grouping individuals, and understanding the characteristics of the identified groups.
This document serves as a quick reference guide for using SPSS. It covers data coding, transferring data from Excel to SPSS, and various SPSS functions for data analysis. Additionally, it provides step-by-step instructions on labeling variables and visualizing results.
The document is a supervised exam focused on distributed systems and their application in big data. It evaluates the understanding of vector clocks, matrix clocks, and token-based mutual exclusion in distributed systems. The exercise requires calculating time synchronization within systems, resolving mutual exclusion scenarios, and schematizing critical section access sequences based on specific conditions. The methodologies demonstrate a combination of theoretical models and practical problem-solving in distributed environments.
The document focuses on analyzing distributed systems using vector clocks and matrix clocks for synchronization. It includes exercises to calculate dependencies and mutual exclusion scenarios with token-based control. Additionally, critical section entry sequences are modeled under different configurations in a distributed setup.
The document contains two exercises focusing on distributed systems. The first exercise involves calculating vector and matrix clocks to track chronological events in the given distributed system. The second exercise addresses mutual exclusion using a token-based control mechanism for a distributed system with four sites, modeling scenarios for ordering critical section access based on token availability and requests.
The document provides an outline of a Business Intelligence project for the 2020/2021 academic year. It details the steps of the process, including selecting business processes, defining indicators, designing a data warehouse with tools like Power AMC, and implementing ETL using SSIS. Furthermore, it involves creating OLAP cubes with SSAS, querying cubes, and generating reports using Power BI. The document also specifies the deliverables, evaluation criteria, and teamworking structure.
This document outlines the structure and evaluation plan for the 'Projet Décisionnel' course for 2020/2021. Key focuses include selecting a business process, designing a data warehouse using tools like Power AMC, implementing ETL with SSIS, creating and querying OLAP cubes via SSAS, and conducting reporting with PowerBI. The project deliverables include interim and final solutions, evaluated through interaction, presentations, and a final defense.
This document outlines the objectives and deliverables of a data warehousing course for the academic year 2020/2021. The module covers the selection of business processes, key performance indicators, and the design and implementation of a Data Warehouse utilizing various tools. Students are required to work in pairs and deliver presentations and reports throughout the course.
This document presents a statistical analysis exercise split into two parts. The first focuses on performing one-way ANOVA tests on decathlon event data to determine competition-based differences in the 100m and javelin events. The second part analyzes 16 years of financial indicators from an oil company, explaining how to construct a data set for Principal Component Analysis (PCA) in SPSS. It identifies variables and observations, evaluates inertia distribution on axes, and explores correlations and patterns in financial data. Key findings include strong negative correlations between EXP a...
This document demonstrates the process of implementing batch processing using Apache Spark. It introduces foundational concepts related to Spark architecture and its relevance in handling large-scale data workflows. Practical examples illustrating data ingestion, transformation, and analytics are provided. The document emphasizes efficiency and scalability in managing complex data pipelines.