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 atelier focused on data mining within the Business Analytics & Data Science program for the academic year 2021-2022. It covers principal component analysis, correspondence analysis, and unsupervised classification methods. Additionally, it provides references to relevant resources such as cran.r-project.org and anaconda.com.
The document focuses on Principal Component Analysis (PCA) as a technique for dimensionality reduction and visualization of high-dimensional data. PCA centers on creating new variables, the principal components, that summarize quantitative data while minimizing information loss. It involves steps like data standardization, eigenvalue decomposition, and projections to lower dimensions, enabling visualization of relationships between individuals and variables. The methodology is applied to datasets of varying scales, emphasizing the identification of homogeneous groups, variable relevance, an...
The document outlines key concepts and methods in Business Analytics and Data Science, focusing on Principal Component Analysis (PCA) as a technique for data summarization. It discusses various methodologies, aims for visualization, and the importance of understanding relationships between variables. The text also emphasizes dimensionality reduction and the significance of centering and scaling data for meaningful analysis.
This document discusses the principles and methodologies of data mining, emphasizing its relevance and application in various fields. It highlights the intersection of statistics and information technology to uncover valuable insights from large datasets. The document also explores different methodologies for effective data mining practices.
This document presents a data mining project focused on classifying road accidents in France for the year 2020. It includes data importation, variable selection, descriptive statistics, correlation analysis, PCA, and K-means clustering. The findings highlight the relationships among various factors contributing to road accidents.
This document presents assessment items on Decision Support Systems focusing on data warehousing concepts like subject-oriented collections, integration, and data historicity. The methodology for practical application is illustrated via the design of a data warehouse schema for healthcare analytics, including primary keys, boolean attributes, and dimensional modeling. A separate practical scenario evaluates vehicle rental contracts using financial metrics to ensure solution scalability and operational service levels. Core calculations about storage dimensions and performance are included to...
This document contains a detailed Big Data exam focusing on Apache Spark, Hadoop, Hive, and Pig. It evaluates knowledge of Spark APIs, functionalities, and components, alongside Hadoop architecture and Hive capabilities. The exam covers practical aspects of data processing, querying systems, and programming paradigms in Big Data frameworks. Core methodologies include in-memory computations, lazy evaluations, and using specific tools for tasks like transformations, data analysis, and SQL integration.
The document elaborates on exponential smoothing techniques, particularly their application in time series forecasting for data with no clear trend or seasonality. It describes simple exponential smoothing (SES) and its extension by Holt and Winters for handling linear trends and seasonality. The simplicity and effectiveness in short-term predictions are highlighted, with practical examples and use of software like R for implementation. Key mathematical formulations, selection of initial values, and evaluation methods like the Mean Absolute Error (MAE) are discussed.
This document focuses on forecasting demand using historical data. It outlines graphical representation of demand trends, development of forecasting models using decomposition methods, and evaluation of model accuracy through error metrics. Additionally, it proposes corrections to forecasts, estimation of future demands, implementation of exponential smoothing methods with different alpha values, and selection of the most accurate model.
This course explores high-performance computing (HPC) and its increasing importance in research and industry, aimed at addressing demanding computational applications. It provides an overview of current hardware systems through their architecture and performance, followed by an in-depth study of parallel computing from algorithmic and programming perspectives. The focus is on efficient algorithm design and automatic parallelization of polyhedral programs (nested loops) using programming environments like OpenMP and MPI. The course emphasizes fostering familiarity with foundational concepts,...
Ce cours aborde le calcul parallèle et distribué, illustrant son importance dans la recherche et l'industrie. Les étudiants apprendront les architectures parallèles et les techniques de programmation parallèle, en mettant l'accent sur la détection du parallélisme et la conception d'algorithmes parallèles. Le module inclut une étude approfondie des outils et environnements de programmation associés.
This document provides a comprehensive guide on how to create and manage a data set in SPSS after conducting surveys. It discusses the critical steps involved in data entry, including the definition and encoding of variables and the practical preparation of the SPSS data file. The document also covers types of questions and their coding, ensuring the data is ready for statistical analysis.
This document serves as an introductory guide to SPSS software, particularly for market research applications. It provides an overview of SPSS functionalities such as data entry, variable configuration, statistical analysis, data visualization, and advanced marketing analytics tools like RFM and clustering. Key menus and commands are detailed, including instructions for navigating data views and utilizing transformation, analysis, and graphical tools. The manual is tailored toward new users aiming to leverage SPSS for streamlined statistical data management and analysis.
This document outlines systematic steps for preparing and encoding a database within SPSS software, including variable definition and encoding. It describes various data input methods based on question types, such as single-answer, multiple-choice, Likert scales, ranking, and open-ended responses. Additionally, it touches on variable property modifications, such as creating new variables and aggregating data. It concludes with guidance for entering and managing data within SPSS, emphasizing the importance of consistent numbering and saving practices.
This document outlines the preparation of data encoding using SPSS software. It includes the definition of variables, data entry procedures, and modifications of variable properties. Specific examples of different types of questions and how they should be encoded in SPSS are provided.
The document centers on evaluating the decision of a clinic to adopt Big Data technologies for its information system. It emphasizes the potential advantages, such as improved data processing and patient care, while addressing associated challenges like system complexity and data security. Practical recommendations are suggested to optimize implementation strategies. The analysis concludes with an overall assessment of the feasibility of the clinic's decision within the given constraints.
This document discusses the decision of a clinic to implement a Big Data-based information system. It outlines the potential benefits and considerations of such a system. Students are required to submit a PDF document detailing their analysis by the deadline.
This document provides a detailed introduction to Hadoop, a core component of Big Data solutions, discussing its history, architecture, and ecosystem. It outlines the framework's features such as distributed file storage (HDFS), fault tolerance, scalability, and cost efficiency, while highlighting how Hadoop addresses the limitations of traditional RDBMS for big data. The ecosystem includes tools like HDFS, MapReduce, YARN, Hive, Pig, HBase, and Spark, among others, which enable efficient data storage, processing, and analysis. Furthermore, it emphasizes Hadoop's main use cases and its adva...
This document provides an introduction to Big Data with a detailed focus on Hadoop. It covers the reasons behind Hadoop's development, its characteristics, ecosystem, architecture, and its advantages and disadvantages. The content is aimed at understanding the fundamental concepts of Hadoop and its relevance in managing large datasets.
The document discusses the limitations of the traditional Hadoop architecture and introduces YARN, a resource negotiator framework aimed at improving resource allocation and job tracking. YARN separates cluster resource management from job scheduling, enabling support for diverse distributed applications beyond MapReduce. Key components include the resource manager, node manager, and containers, which work collaboratively to execute tasks efficiently. YARN also extends Hadoop's capabilities to support frameworks like Spark, Giraph, HBase, and Tez for handling large-scale data processing and...
This document introduces the architecture of Hadoop and the role of YARN (Yet Another Resource Negotiator) in resource management and job scheduling. It explains the modifications in Hadoop 2.x that allow for the execution of various distributed applications beyond MapReduce. Key functionalities and components of YARN are discussed, including the resource manager, application master, and node manager.
The document provides an in-depth explanation of the WordCount application built with Hadoop MapReduce, detailing its components, including the Java classes and methods used for program execution. It describes how data flows through Mapper and Reducer classes to produce word frequency counts, utilizing HDFS paths for input-output configuration, regex patterns for parsing, and several Hadoop libraries for functionality. The methodology highlights robust error tracing via Logger and efficient key-value processing with Writable types for distributed computing tasks.
This document provides an overview of the WordCount v1.0 source code used in Hadoop MapReduce. It describes the essential Java classes, including Mapper and Reducer, and the configuration needed to run a MapReduce job. Additionally, it highlights the use of logging for tracking the job's success and offers insight into setting input and output paths in HDFS.
The document provides an in-depth explanation of the MapReduce programming model, inspired by functional programming paradigms, to perform parallel data processing over massive datasets. It explains the core principles of 'map' and 'reduce' operations, demonstrating them with word count examples. The paper details the MapReduce process, such as data fragmentation, parallel computation, and aggregation of results, alongside its architecture within frameworks like Hadoop. It also highlights the advantages, critiques, and practical applications of the model, finishing with the programmer's rol...