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 exploration of classification techniques within data mining, focusing on methods such as k-means and hierarchical clustering. It describes the definition, key properties, and applications of classification, emphasizing the process of grouping objects into homogenous clusters. Various evaluation criteria and methods for measuring classification quality, such as interclass and intraclass inertia, are explained. Practical examples, including a step-by-step implementation of k-means clustering, are provided to illustrate the methodology.
The document summarizes the principles and applications of correspondence analysis methods (AFC and ACM). It highlights their advantages, such as transforming qualitative variables into quantitative ones, handling nonlinear relationships, and visualizing variable dependencies and patterns. Case studies like store client segmentation are leveraged to compute metrics such as eigenvalues and contributions, with detailed analysis for dimensions and axes. The explanation includes the retention of significant axes based on inertia distribution, with practical insights into variable contributions...
This document explores fundamental factorial analysis techniques including PCA, FCA, and MCA, as preliminary methods for multivariate analysis. PCA focuses on quantitative variables and projects data into lower-dimensional subspaces while preserving distances between individuals. FCA is applied to contingency tables to analyze relationships between qualitative variables, and MCA generalizes FCA to account for more than two qualitative variables using disjunctive tables and Burt tables. The document outlines mathematical foundations, steps for performing factorial analyses, and graphical int...
This document thoroughly explores the methodologies and multiple phases of data mining (DM), including defining study objectives, data collection, preprocessing, and predictive modeling. Emphasis is placed on data preparation methods such as handling missing or extreme values, detecting multicollinearity, and evaluating data distribution characteristics through techniques like normalization and discretization. Statistical tools for detecting anomalies and variable relationships, along with advanced analysis techniques like regression and variance analysis, are discussed. Additionally, sampl...
This document reviews the functionality, creation, and advanced utilization of PivotTables (Tableaux Croisés Dynamiques, TCD) in Excel. Part 1 introduces foundational concepts including TCD creation, formatting, and statistical computation. Part 2 explores data source management such as complex multi-sheet integration and relational modeling using advanced Excel features like Queries and Models. Emphasis is placed on organizing data sources effectively and leveraging advanced formula functions within TCD frameworks for dynamic analysis. It draws upon the author's extensive professional inst...
This document outlines a comprehensive course on natural language processing (NLP) using artificial intelligence tools. It covers advanced topics such as corpus construction and text exploration, statistical and neural approaches to word representation, sentiment analysis, topic modeling, and chatbot creation. The course emphasizes theoretical understanding, practical exercises, and application development, leveraging modern tools like Python, TensorFlow, and NLP libraries. Practical workshops focus on real-world applications, such as text classification, sentiment modeling, and web-based c...
This course covers the fundamentals of Natural Language Processing (NLP) using artificial intelligence tools. It provides knowledge on formal grammars, data analysis, and AI techniques for NLP applications. Students will engage in practical workshops to develop skills in web applications, sentiment analysis, topic modeling, and chatbot creation.
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 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 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...
This document introduces the concept of Big Data and the MapReduce programming model. It covers the principles, functionality, and applications of MapReduce in parallel data processing. Additionally, it discusses the advantages and critiques of the model.
The document introduces the concept of Big Data and Hadoop, specifically focusing on the HDFS (Hadoop Distributed File System). It proposes a practical task to manage and distribute a large file (850 MB) containing baby names and birth data within a Hadoop cluster. The cluster setup includes five slave nodes, one master node, and a default replication factor of three. The methodology involves designing an appropriate HDFS architecture, detailing the components of HDFS, and explaining how the file will be distributed across the nodes.
This document introduces the topic of Big Data and focuses on Hadoop, particularly its architecture for handling large files. It details the characteristics of the 'babynames.txt' dataset, which includes baby names, genders, and birth dates for 2020, and outlines the task of proposing an HDFS architecture for this dataset.
This document introduces Big Data concepts, focusing on Hadoop's ecosystem and HDFS architecture. It outlines a practical exercise requiring the distribution of a large dataset—a babynames.txt file—across a Hadoop cluster consisting of one master node and five slave nodes. The exercise emphasizes configuring default replication (set to 3) and explaining how HDFS components manage data distribution. The aim is to demonstrate efficient data storage and processing in distributed systems.
This document discusses the architecture of HDFS for storing a baby names dataset containing gender and birth dates for the year 2020. It outlines the setup of a Hadoop cluster consisting of one master node and five slave nodes, along with the replication strategy for data storage. It requires delineation of HDFS components and file distribution across the nodes.
This document by Dr. Abir KHALDI introduces Big Data, focusing on its historical evolution, challenges, and the inherent complexities it addresses. Key themes include the 3Vs (volume, velocity, variety), the Lambda architecture for hybrid data processing, and core technologies like Hadoop, Spark, and NoSQL. It discusses problems of storage, processing, and data collection, alongside their solutions, and explores applications in domains like behavioral analysis and targeted marketing.
This document introduces the concept and challenges of Big Data, including its historical background and the significance of the 3Vs: volume, velocity, and variety. It discusses various technologies associated with Big Data, such as Hadoop, MapReduce, and Spark, while outlining key applications and processing techniques.
This document explores machine learning concepts, specifically focusing on supervised learning using decision tree methodologies. It explains the principles of tree construction, including attribute selection, pruning, and segmentation. Metrics such as entropy and information gain are detailed as criteria for evaluating node splits during the tree induction process. The document extensively uses examples, including Quinlan’s ID3 algorithm, to elaborate on theoretical concepts and practical applications of decision trees.
This document outlines the schedule and topics to be addressed in a Deep Learning course held in November 2021. The syllabus integrates supervised learning concepts, probabilistic approaches, and tasks related to linear regression. Essential pre-requisites include foundational knowledge in probabilities and numerical approaches, with encouraged preparation focusing on specific chapters. Timed evaluations and opportunities for questions are included to enhance learning efficacy.
The course introduces machine learning methodologies, focusing on supervised learning approaches such as regression, Bayesian decision rules, and k-nearest neighbors, alongside unsupervised techniques like K-means and association rules. Statistical methods are explored for data interpretation, while the concept of decision surfaces and hyperplanes is key for classification. The curriculum additionally covers both parametric and non-parametric approaches, emphasizing practical interpolation over statistical assumptions. Finally, deep learning is outlined through neural network applications.
This document provides a systematic exploration of the foundational principles and methodologies underpinning machine learning. It delves into supervised learning, illustrating techniques like decision trees and linear regression through visual examples, and introduces advanced forms such as reinforcement learning. Key insights into unsupervised learning methodologies, including clustering and association rule extraction, are elucidated with practical applications. The content concludes with evaluation strategies tailored for model validation using both statistical methods and domain expert...