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 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 details the procedure for creating an Extract, Transform, Load (ETL) solution using SQL Server Integration Services (SSIS). It includes instructions for restoring backup files, utilizing SQL Server Management Studio, and configuring a local environment. The methodology emphasizes the preparation of databases and execution of sequential tasks across multiple exercises. Findings suggest a step-by-step approach for successful ETL implementation within the (local) SQL Server environment.
This document provides an in-depth tutorial on decision trees, focusing on the ID3 and C4.5 algorithms developed by Ross Quinlan in the 1980s and 1990s. It introduces the foundational concepts of machine learning and supervised learning, explaining how decision trees classify objects based on attributes. The tutorial includes theoretical descriptions, practical examples, pseudo-code demonstrations, and Python3 guidance for implementation. Through structured learning and case studies, readers can develop robust conceptual and practical knowledge in decision tree methodology.
The document presents an exercise on constructing a decision tree using the ID3 algorithm, with explicit instruction to utilize provided data for training. It emphasizes methodical calculation of entropy and information gain at each step. The final output is expected as a visual representation of the tree structure. The example is taken from a university examination by M. Mourad Loukam at the Université de Chlef, Algeria.
This document explains the construction and evaluation of a decision tree using Quinlan's ID3 algorithm with gain functions based on Shannon entropy. It covers step-by-step calculations for entropy, gain, and tree-building logic, ultimately leading to a perfect classification on the training data with zero empirical error. The process is extended to a validation set to estimate prediction error, which is calculated at 25%. Key techniques include the computation of conditional entropy and homogeneous node checks to stop recursion.
The document provides an exercise focusing on constructing a decision tree using Quinlan’s ID3 algorithm. It outlines sample data with three attributes—size, hair color, and eye color—distributed across two classes. Participants are required to calculate entropy, information gain, and empirically evaluate misclassification errors. The task includes constructing an entropy-based decision tree with homogeneity as the termination criterion and testing prediction errors using a separate test dataset.
The book introduces key concepts and methodologies in data mining and machine learning, bridging the gap between theory and application. Authored by Ian H. Witten and Eibe Frank, it explores topics such as decision trees, association rules, and neural networks, emphasizing their practical usage in various fields. It incorporates the Weka toolkit to guide readers through machine learning workflows, including data preprocessing, model building, and evaluation. The text balances theoretical rigor with hands-on examples, making it a resourceful guide for both newcomers and experienced practitio...
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.
This exercise document explores the application of linear regression using data from 24 Paris apartment sales in 1975. It begins with data visualization requirements, specifically assessing the scatter plot form. Statistical computations, including mean, variance, and covariance, are calculated for the dataset. The linear regression model is then fitted using the least squares method, with a focus on estimating the coefficients for the linear equation. Finally, the model is used to predict apartment prices for specified surfaces, providing interpretations of the results.
The document provides a thorough exploration of probabilistic classification methods, including the majority rule, maximum likelihood rule, and Bayesian rule. It explains key concepts such as how probabilities are defined within datasets and highlights the differences between these methods in terms of accuracy and applicability. The Bayes rule is specifically derived and compared to other methods, presenting its scenarios where it simplifies to maximum likelihood. Finally, error computations for each classification method are detailed to demonstrate their effectiveness.
The document focuses on probabilistic approaches in classification, emphasizing the use of Bayesian decision rules. It evaluates weather predictions using barometric data, exploring how prior and conditional probabilities combine to predict outcomes. Additionally, it addresses the classification of a population based on logical attributes, examining three methods: majority rule, maximum likelihood, and Bayesian classification. The study highlights the strengths and weaknesses of each method, employing mathematical and probabilistic techniques to calculate prediction errors and assess classi...
This document examines probabilistic approaches in the context of machine learning, focusing on Bayesian decision-making. It introduces a weather prediction problem based on barometer readings using supervised learning, where Bayes' theorem is applied to derive conditional probabilities. The methodology includes calculation of posterior probabilities to classify observations and assess the reliability of predictions. The exercise concludes with a demonstration of the Bayesian classification process, reaching a prediction reliability of 67% when forecasting rain based on barometer observations.
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...
This document evaluates knowledge on probabilistic classification methods including majority decision rules, maximum likelihood, and Bayes rule. It involves analyzing a dataset with patients divided into two classes (healthy and sick) based on binary attributes (blood pressure and cholesterol). Key calculations include determining probabilities for specific cases and developing classification rules for one or two attributes. The exam also emphasizes clarity in responses and detailed explanations for methodology and results.
The document outlines fundamental concepts of artificial intelligence (AI) and machine learning (ML), including definitions, disciplines, and methodologies. It introduces the main categories of AI (cognitive and pragmatic approaches), describes supervised and unsupervised learning techniques, and discusses practical use cases such as decision support systems, natural language processing, and pattern recognition. Emphasis is placed on the importance of extracting knowledge from data through processes like classification, clustering, and association rule learning, with illustrative examples o...
This document outlines a research project focused on collecting labor time data in pilot viticulture farms while overcoming the lack of digital tools for field workers. A standardized glossary of operations ensures uniform data collection, with manual entries on predefined notebooks verified by farm managers before being digitized into a central application. Data integration is monitored by the project researcher, and periodic reports provide insights for workers and phytosanitary management. At year-end, a comprehensive analysis synthesizes operational insights, shared with all involved fa...