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 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.
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.