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.
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...
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 highlights the development of a decision support system for the Arab Tunisian Bank (ATB). Agile methodologies, particularly SCRUM, were employed in conjunction with Business Intelligence tools to create an optimized DataWarehouse and Datamart for analyzing client risk and asset classifications. The solution involved designing conceptual models, exploring ROLAP, MOLAP, and HOLAP approaches, and implementing Extract-Transform-Load (ETL) processes. The outcomes included enhanced data management and reporting capabilities for better financial risk assessment and client monitoring.
This report documents the development of a project aimed at generating calculations of mobile data consumption using advanced tools and methodologies. The authors evaluate existing organizational practices in OOREDOO Tunisie, propose solutions based on SCRUM BI methodology, and employ a robust Data Warehouse using tools such as Talend and Oracle Database. Key outcomes include cleaning, transforming, and integrating large data into an optimized structure, culminating in the creation of interactive dashboards for visualizing subscriber data and mobile data consumption trends.
This document details the development of a solution for estimating the prices of used cars in Tunisia. It follows an agile methodology using SCRUM, incorporating machine learning for price prediction. Core tasks include web scraping from various Tunisian car advertisement websites, data cleaning, and the implementation of linear regression and gradient regression models. The project concludes with the centralization of data in a SQL database and the development of a web interface for user interaction.
This document presents a final year project focused on the study, design, and implementation of a business intelligence dashboard system using Big Data technologies. The project uses tools like Apache Hadoop, Spark, Hive, and Arcadia Data to conceptualize and create dashboards from empirical data collected via probes (Astellia and Otarie). Key methodologies like Agile, GIMSI, and Balanced Scorecard were adopted. The result demonstrates a scalable and functional data visualization solution, offering performance indicators and insights tailored to the needs of the host organization, Orange Tu...
This document details the implementation of a Business Intelligence (BI) tool within the Odoo ERP system. Using the SCRUM methodology, it involves the design and development of various data warehouses to address distinct use cases: sales, purchasing, and stock management. The report also covers the integration of PostgreSQL for data storage and manipulation, and Power BI for visualization. Key findings include the establishment of dynamic dashboards enabling efficient data-driven decision-making for the company WAKAHAW.
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.
Partie 2 - Introduction Apache Spark Apache Spark - Pr sentation Apache Spark est une plateforme de traitement sur cluster g n rique. C'est un moteur de traitement libre, assurant un traitement parall le et distribu sur des donn es massives.
The document introduces supervised classification techniques, emphasizing decision tree algorithms. It compares traditional expert systems to machine learning-based classification, highlighting the inductive approach to generating classification rules. Decision trees are presented as interpretable models, with graphical representation and rule derivation, using attributes like temperature or age for medical and customer profiling scenarios. The text further describes algorithmic frameworks like ID3 and CART, explaining entropy and Gini index for node splitting, and concludes with a general...
This document provides an interactive quiz format to introduce key concepts in data mining, covering its predictive and descriptive applications. It addresses business motives behind data mining, such as uncovering hidden trends and enhancing profitability. Core techniques like decision trees, neural networks, and association rule mining are highlighted, alongside challenges such as ensuring privacy and overcoming technical limitations. Practical applications and critical success factors for data mining in various industries are also explored.
This document provides an overview of decision trees with a specific focus on the Gini Index as a metric. A banking use case illustrates predicting customers' loan repayment capacity through a decision tree framework. The step-by-step construction of the decision tree is documented, emphasizing rules extraction. Strengths and weaknesses of decision trees are critically analyzed, shedding light on their advantages and inherent limitations.
This document provides an introduction to data mining, focusing on its definition, interdisciplinary nature, and applications across various domains. It highlights the methods and algorithms involved in extracting meaningful insights from vast datasets and emphasizes the importance of data preparation. The applications of data mining include fraud detection, risk management, customer analysis, and healthcare diagnostics.
This document provides a comprehensive overview of classification and clustering methods in data science. It distinguishes between supervised and unsupervised learning, explaining criteria for optimal clustering with similarity measures and specific metrics like Manhattan and Euclidean distances. The non-hierarchical methods, including k-means clustering and dynamic clouds, emphasize minimizing intra-class inertia and provide examples of their application in market segmentation. Hierarchical clustering techniques are also explored, including dendrogram construction to visualize classificati...
This document introduces the MapReduce paradigm, a distributed computing model to process large datasets, especially in scenarios involving tasks such as data aggregation and analysis. Traditional sequential methods are contrasted with the efficiency and parallelization enabled by MapReduce, with emphasis on how large-scale problems can be divided into smaller subtasks. The two core operations, MAP and REDUCE, are described in depth, along with examples in word frequency counting, web analytics, and finding mutual connections in social graphs. The advantage of automating data distribution a...
This document explores the MapReduce paradigm, designed for processing large data sets through distributed computing. It discusses the traditional sales aggregation problem, outlining inefficiencies and presenting MapReduce as a solution. The framework allows for efficient problem-solving by breaking down tasks and parallelizing the computation necessary for big data analysis.
This document provides an in-depth explanation of the MapReduce paradigm used in large-scale distributed computing. It describes the inefficiencies of traditional solutions to analyze large datasets and introduces MapReduce as an effective alternative. The methodology involves dividing tasks into smaller, parallelizable operations: `MAP` to transform data into key-value pairs and `REDUCE` to aggregate results by key. Various examples, including word frequency analysis, website statistics, and common connections in social graphs, illustrate practical applications of the MapReduce model. The...
This document provides an overview of the MapReduce programming model and its efficiency in processing large datasets. It discusses the traditional methods of data processing and introduces the MapReduce paradigm, emphasizing its divide and conquer approach. The document outlines the operations of MAP and REDUCE, detailing the steps involved in the MapReduce process.
Hadoop is an open-source framework designed for distributed data processing across massive datasets, with scalable and fault-tolerant architecture. Core components include HDFS for distributed storage and MapReduce for distributed computing, with additional tools like Hive, Pig, and HBase for advanced analytics and data management. Inspired by Google's publications, Hadoop leverages principles like data redundancy, clustering, and parallel processing to support Big Data challenges. It has been widely adopted across industries, including by organizations such as Facebook, Google, and Amazon.
Hadoop, an open-source framework managed by Apache and written in Java, facilitates distributed processing of massive datasets using clusters of commodity hardware. Key components include HDFS for distributed storage and MapReduce for parallelized data processing, ensuring fault tolerance, scalability, and cost-efficiency. The ecosystem extends functionality through tools such as Pig, Hive, and Flume for data processing, storage, scheduling, and monitoring. Developed by Doug Cutting and inspired by Google technologies, Hadoop powers numerous enterprises like Amazon, Adobe, Facebook, and Twi...
Hadoop is an open-source framework designed for distributed processing of large data sets across clusters. It facilitates the creation of applications that handle massive volumes of data while ensuring performance and fault tolerance. The technology has evolved under the Apache foundation and is widely used in various enterprises.
This document provides an in-depth critique of MapReduce's limitations, emphasizing its inefficiencies in complex, multi-step computations. It introduces Apache Spark as a more advanced, memory-optimized solution with higher performance, scalability, and interactive APIs for diverse programming languages. Key Spark components like RDDs, DataFrames, Datasets, and its ecosystem libraries (e.g., MLlib, GraphX) are detailed alongside their integration capabilities. The document further outlines Spark's architecture, including its execution model and resource management, while contrasting its pe...