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 covers a course on Data Mining focusing on Decision Trees. It includes definitions, examples, and how to construct a decision tree. Key methods of classification such as ID3, C4.5, and C5.0 are also discussed.
This document provides an overview of supervised classification using decision trees. It includes an introduction to the structure of decision trees and details the ID3 algorithm along with its characteristics. Additionally, the document references course notes and literature related to the topic.
This document covers supervised classification methods focusing on representative examples, particularly the k-Nearest Neighbors (kNN) algorithm. It explores the concept of dissimilarity measures and their importance in classification tasks. Practical applications using Weka and reference materials are provided for further study.
This document contains notes from a data mining course focused on supervised classification methods. It includes a discussion of methods like OneR and Naive Bayes, their applications, and exercises involving datasets. Key concepts such as probability estimation in classification are also addressed.
This document presents notes from a course on Data Mining, focusing on supervised classification methods. It covers the basics of classification, including the ZeroR and Naïve Bayes methods. The aim is to explain the process of constructing classifiers and evaluating their performance.
This document discusses the classification of texts using the Naive Bayes method, focusing on the detection of spam emails. It covers the problem and the data used for analysis, along with the representation of texts and the supervised classification process. The solution includes normalizing the texts, constructing a vocabulary, and extracting attributes for classification.
This document outlines a lab exercise focused on applying the Naïve Bayes method for text classification. It involves working with a dataset of emails to classify them as spam or non-spam. The exercise includes steps for text representation, probability calculation, and classification of new emails.
This document outlines an exam exercise focused on data mining techniques applied to the 'play tennis' dataset. It covers the application of simple classification methods such as ZeroR, OneR, and Naive Bayes using Weka. Students are required to classify instances and evaluate the performance of different classifiers using specified training and testing samples.
The document consists of exercises that explore decision-making models using graphical representations. It includes scenarios where individuals must make strategic decisions based on uncertain outcomes, using concepts from probability and utility theory. The exercises guide the reader through creating decision trees and Bayesian networks for optimization.
This document covers the fundamentals of data mining, including various data types and attributes. It also discusses supervised classification, decision trees, and Bayesian classifiers. Exercises and examples are provided to reinforce the concepts.
The document provides an overview of Artificial Intelligence (AI), its definition, and goals, including modeling human reasoning processes and reproducing human behavior. It highlights the Turing Test and philosophical critiques like the Chinese Room argument, differentiating approaches to evaluating machine intelligence. It reviews AI's historical milestones—from early programs like ELIZA to modern developments in natural language processing, machine learning, and robotics. Additionally, it explores societal applications of AI, such as recommendation systems, automated vehicles, and roboti...
Ce document présente une introduction à l'intelligence artificielle, définissant son but et abordant des concepts fondamentaux comme le test de Turing et l'argument de la chambre chinoise. Il explore deux écoles de pensée sur l'intelligence des machines et fournit des exemples concrets, comme le jeu d'échecs, pour illustrer ces idées. La discussion inclut également un bref historique de l'IA de la période 1945-1955.
This document presents a series of exercises focused on problem representation and resolution techniques in artificial intelligence. It covers state space representation for various problems, including the monkey and bananas problem, robot navigation, and graph representation of the cube universe problem. It requires the application of depth-first, breadth-first, hill climbing, best first, and branch and bound algorithms.
This document discusses the development of classification methods based on formal concept analysis using the WEKA platform. It emphasizes the importance of decision-making in data handling and showcases various data mining techniques. The presentation concludes with perspectives on future applications and methodologies.
This document introduces the field of business intelligence (BI) and data warehousing, addressing the need for extracting actionable insights from large datasets supporting decision-making processes. It outlines the evolution of data-driven decision systems (e.g., OLAP, ERP) and compares operational computing (OLTP) with decision-oriented computing (OLAP). Challenges in data coherency, heterogeneity, and modeling are discussed. Additionally, it highlights methodologies, tools (e.g., ETL, data mining), and platforms (e.g., SAP, Oracle) critical for successful BI projects, focusing on tools t...
This document discusses a coursework assignment focusing on text classification using the Naïve Bayes method. It involves analyzing a dataset of emails marked as spam or non-spam and aims to build a classifier to detect spam. The assignment covers the representation of texts and the application of Naïve Bayes classification.
This document outlines the design and implementation of a BI solution integrated with Sage ERP. The methodology involves extracting and consolidating enterprise data into dashboards for better decision-making. Data is processed through ETL tools, emphasizing database integrity and visualization via tools like Tableau. The solution aims to enhance organizational performance metrics and support better strategic decisions.
This document details the design of a business intelligence solution for companies using Sage ERP. It involves creating interactive and automatically generated dashboards based on the Sage database. A preliminary analysis of Sage functionalities, manual dashboard practices, and BI principles guided the development. The resulting solution assists companies in making strategic decisions by leveraging generated reports and dashboards.
The document explains the process of classification using decision trees, starting from traversing nodes to reach a decision at leaves. It details the ID3 algorithm, emphasizing entropy and information gain for optimal attribute selection at each node. Practical examples illustrate the construction of decision trees, including scenarios of choosing attributes like age, income, and gender, and summarizing classification pathways. Additionally, the limitations of the greedy nature of ID3 and extensions to regression trees using variance minimization are discussed.