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.
Ce chapitre présente les concepts fondamentaux des systèmes interactifs d’aide à la décision (SIAD) et leur impact sur le processus décisionnel. Il souligne l'importance de l'aide à la décision par rapport à la prise de décision automatique et discute des caractéristiques et des avantages des SIAD. En outre, il aborde l'évolution des SIAD et leur utilisation dans des contextes de décision complexes.
Le document présente l'historique et les définitions des systèmes experts, des programmes conçus pour imiter le raisonnement humain dans des domaines spécifiques. Il décrit également des exemples de systèmes célèbres, la méthodologie employée, ainsi que les objectifs de capture et d'exploitation des connaissances. Il aborde l'évolution des techniques en intelligence artificielle visant à résoudre des problèmes complexes.
The project involves creating a smart pot that autonomously waters plants and alerts the owner in case of issues. It includes developing an expert system that classifies plants based on their characteristics to determine watering frequency. The project requires research, software environment selection, and implementation of the expert system.
This course introduces the concepts of Business Intelligence (BI) and its relevance in data analysis for businesses. It covers data extraction, processing, and the use of BI tools while providing insights into real-world applications. The course also includes evaluations through homework and a final exam.
The document introduces the concept of data warehouses as specialized data storage systems designed to support strategic decision-making through integrated, subject-oriented, non-volatile, and dated data. It outlines core methodologies like ETL processes for extracting, transforming, and loading data, as well as the architectural design zones such as staging areas, storage zones, and presentation layers. The roles of data marts as subsets serving specific business functions are discussed, alongside advanced modeling techniques, including dimensional models like star and snowflake schemas. P...
The document elucidates the concept and architecture of data warehouses (DW), emphasizing their utility for strategic and tactical decision-making by non-technical stakeholders. It provides insight into the fundamental features of DW, including non-volatility, integration, time orientation, and subject orientation, complemented by detailed discussions on ETL (Extract, Transform, Load) processes and data modeling techniques such as star and snowflake schemas. Additionally, it explores practical applications of DW across industries like finance, healthcare, and logistics and briefly compares...
This document outlines the foundational principles of data warehousing by introducing a business case for consolidating sales data from a distributed system into a central repository. It provides a comprehensive explanation of the dimensional modeling process, including the creation of a star schema with fact and dimension tables. Various analytical methodologies like slicing/dicing, aggregations, and hierarchies in multidimensional data cubes are discussed. The critical trade-offs between conceptual simplicity and technical storage efficiency in the use of aggregation tables are also highl...
The document describes Minitab Statistical Software, originally developed at the University of Pennsylvania in 1972. It was initially targeted at students but gained wide adoption in industries for Six Sigma DMAIC methods. The software provides tools for data manipulation, statistical analysis (e.g., ANOVA, regression, hypothesis testing), and graphical representation, with integration of specialized features for quality analysis. Users can also customize the interface with personalized toolbars and keyboard shortcuts for expedited task execution.
This document presents a comprehensive exploration of graphical tools available in Minitab, categorized by analytical objectives such as relationships between pairs of variables, distribution evaluation, and temporal data visualization. It details specific graph types like scatter plots, histograms, and box plots with methodology for customizations and statistical analysis integration. The document includes practical examples for using these graphs in real-world applications, such as assessing distribution symmetry or comparing multi-variable relationships. Furthermore, it provides step-by-...
The document describes a practical case study centered on designing and implementing a data warehouse for a fictitious global sports goods company, Orion. It covers the company’s organizational, product, and customer informational landscape and establishes questions that the Business Intelligence system aims to answer, focusing on performance metrics and decision support. The solution involves leveraging an operational database and preparing a star schema warehouse with dimensions and fact tables, which will be populated using ETL processes created within Talend Open Studio. Practical instr...
This document presents fuzzy logic, highlighting its origins, applications, and theoretical foundations. It discusses examples of fuzzy rules and how fuzzy logic can model human behavior and handle uncertainty. Applications include decision support, form recognition, and fuzzy control systems.
This document explores expert systems as a subset of artificial intelligence, aiming to replicate human expert reasoning through a knowledge base, rule base, and inference engine. Techniques for deduction include forward chaining, backward chaining, and mixed chaining. It discusses tools such as NEXPERT OBJECT, CLIPS, and Prolog, highlighting their strengths, paradigms, and integration capabilities. The application of expert systems spans domains like medicine (diagnostics), aerospace (traffic planning), and IT (programming assistance).
This document provides an introductory tutorial on importing and reading tabular data formats (CSV, Excel) in R, detailing commands like `download.file`, `read.csv`, and the use of the external `readxl` library for Excel files. It explains how to access rows, columns, and subsets of data using dataframes, demonstrates built-in datasets in R, and explores additional capabilities like inspecting structure (`str`) and accessing complex calculations for data manipulation. Methods for installing and utilizing libraries are also covered comprehensively.
This document discusses expert systems within the scope of artificial intelligence, focusing on their definition, components, and specific algorithms such as forward/backward chaining for inference. It details the development tools, such as CLIPS, NEXPERT OBJECT, and Prolog, highlighting their features, principles, and use cases. The document also explores the applications of expert systems in domains like medicine and aeronautics. It concludes by emphasizing the specialized nature of expert systems and their limited objective evaluation.
This document presents a comprehensive overview of quantitative research methodologies, aimed at gathering measurable and statistical data to describe or explain phenomena. It details sampling techniques, including probabilistic and non-probabilistic methods, alongside strategies for questionnaire design to ensure efficient data collection. The text emphasizes the importance of pretests and proper coding frameworks for enhancing data accuracy and discusses the application of SPSS software for data analysis, spanning univariate, bivariate, and multivariate techniques. It concludes with insig...
This course provides an understanding of data warehousing concepts and the management of data systems. It emphasizes the importance of mastering the fundamental notions related to data warehousing and the ability to model and design a data warehouse. Students will learn to manipulate tools used for data warehousing through practical applications.
The document contains exercises focusing on decision-making models in various scenarios, including production decisions in response to milk shortages, weather forecasting for carrying an umbrella, and decision-making at a tennis tournament. Each exercise requires calculations of expected utility and comparisons of different decision models.
This document contains an exam for the Master 1 program in Computer Science at Université Paris 7, focusing on Artificial Intelligence. The exam consists of exercises related to search algorithms and heuristics, including A* search and alpha-beta pruning techniques. Students are required to analyze heuristics for admissibility and dominance, as well as apply search strategies to find optimal paths in given scenarios.
The document explores the concept and structure of expert systems, highlighting their role as tools that replicate human expert reasoning in specific domains. It delves deeper into expert system generators, explaining their composition, such as knowledge bases, inference engines, and interfaces, and reviews notable examples like ADA, Prolog, and Jess. The applications of these technologies, ranging from medicine to industrial systems, are emphasized. Additionally, the document covers the evolution and interoperability of related programming languages like ADA and Prolog in system design.
This article addresses the problem of video segmentation by proposing a novel method that combines active contours with watershed presegmentation. The method aims to refine segmentation results by introducing a force of attraction to the watershed boundaries, enhancing both precision and regularity. The two-step process helps in accurately detecting moving objects in videos, yielding smoother results than existing methods.
Ce document traite de la détection de points d’intérêts dans les images, un processus essentiel dans la vision par ordinateur. Les méthodes de détection sont classées en trois catégories : basées sur les contours, sur l'intensité, et des approches basées sur des modèles. Le détecteur de Moravec est également présenté comme une méthode pour identifier les changements d'intensité dans une zone considérée.
Ce document traite des différentes catégories de raisonnement en intelligence artificielle, notamment le raisonnement déductif, inductif et abductif. Il aborde également la question de l'incertitude et de l'approximativité dans les raisonnements, ainsi que l'importance du temps dans les systèmes d'IA. Enfin, il explore diverses logiques et méthodes pour traiter ces aspects dans le domaine de l'IA.
Ce document explore les notions du raisonnement en intelligence artificielle, en se concentrant sur les symboles et la représentation des connaissances. Il aborde l'historique et les définitions des ressources nécessaires pour un raisonnement efficace. Différents types de raisonnement, ainsi que leurs techniques et défis, sont également discutés.
This document discusses classification methods in data mining, focusing on Naive Bayes with numeric attributes and examples regarding probability calculations. It also addresses the limitations of Naive Bayes with nominal attributes and introduces decision tree algorithms such as ID3, C4.5, and C5.0 for numeric attributes.