Intelligence artificielle et données
488 documents à télécharger gratuitement
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 document contient des exercices sur l'inférence floue dans le cadre d'une matière d'intelligence artificielle. L'exercice porte sur l'application de la méthode Max-Min et l'évaluation des températures à l'aide de règles d'inférences. Il inclut également des représentations graphiques pour illustrer les fonctions d'appartenance.
This document outlines coursework focused on fuzzy logic, specifically applied to quality control in manufacturing and temperature regulation in buildings. It includes exercises that require the use of fuzzy sets and decision-making processes. Students are tasked with graphical representation of fuzzy subsets and formalization of membership functions.
Ray Kurzweil, Director of Engineering at Google, predicts that by 2029, robots will be smarter than humans. He asserts that advancements in AI will enable computers to learn from experiences, understand humor and stories, and even flirt, marking unprecedented growth in computational power. Kurzweil emphasizes a shift in societal belief, where concepts once considered radical are now widely accepted, citing real-world applications like Google's self-driving cars and Siri. He forecasts that AI intelligence will eventually be a billion times more powerful than the human brain.
This document pertains to coursework for fourth-year students enrolled in data science and INFINI at École Supérieure Privée d’Ingénierie et de Technologies during the academic year 2020-2021. It outlines an exercise labeled '1,' suggesting practical engagement or problem-solving as part of the curriculum. The methodology likely centers around applied learning and computational techniques pertinent to data science. Findings or outcomes are implied to be results of completing assigned exercises.
This document presents an analysis of six brands of orange juice evaluated by a panel of experts based on sensory variables. The evaluation includes various characteristics such as odor intensity, taste intensity, and sweetness. Additionally, a principal component analysis is performed to explore the relationships among the juices.
This document discusses the application of the Naïve Bayes method for text classification, specifically for identifying spam emails. It includes an introduction to the problem, representation of texts, and details on how to construct a Naïve Bayes classifier using a set of labeled email data. Additionally, it provides guidance on normalizing text data and calculating conditional probabilities for the classifier.
This document presents the course 'Deep Learning and Applications,' conducted by Amaury Habrard at Université Jean Monnet. It covers topics such as auto-encoders and generative adversarial networks, along with their applications in computer vision. The course aims to explore advanced concepts in deep learning and their practical implications.
This course covers key concepts in deep learning including convolutions, recurrent neural networks, and attention mechanisms. It includes practical applications and foundational theories. The course is designed for students to gain hands-on experience with various deep learning models.
This course aims to go beyond basic neural networks to explore more complex architectures. Students will learn to work with deep learning frameworks and engage in practical sessions and projects, culminating in a report and defense. The course covers various topics, including Convolutional Neural Networks, Auto-encoders, and Generative Adversarial Networks.
This document presents a comprehensive overview of Information Retrieval concepts, including vocabulary and postings lists, index construction, and tokenization. It covers essential techniques like stop words removal, normalization, lemmatization, and stemming. The content appears to be aimed at Master students, specifically in the context of a course led by Michel Beigbeder.
D ISCOURS DE SOUTENANCE Madame la pr sidente du jury, madame et messieurs les membres du jury, chers coll gues, chers amis, Remerciements Avant de me lancer dans le vif de la pr sentation de mon travail de doctorat, je souhaiterais remercier les membres du jury d avoir port int r t mon tude et d avoir accept de participer cette soutenance.
M moire de Mast re Pour obtenir le mast re en nouvelles technologies de t l communication et r seaux Th me : Conception et d veloppement d un site web de e- commerce pour le compte de LSAT_Nokia R alis par : Adel RAISSI Encadr par : UVT : LSAT_Nokia : Melle Maroua CHAABANI M.
1- ADO. NET (Activex Database Object)(1) Pr sentation Initialement chaque type de gestionnaire de base de donn es avait ses instructions, sa mani re de fonctionner ADO.
Comment les nombres n gatifs sont-ils repr sent s ? Quel est le plus grand nombre qui puisse tre repr sent par un mot machine ? Que se passe-t-il si une op ration g n re un nombre plus grand que ce qu'il n'est possible de repr senter ?
Ce document explique les principes de classification hiérarchique et non hiérarchique, en détaillant les méthodes de calcul des distances et les processus de regroupement des individus. Il aborde également les différences entre méthodes ascendantes et descendantes de classification, ainsi que les implications de l'évaluation de dissimilarité entre groupes et individus.
This document aims to explain the role of hidden layer neurons in perceptrons, using artificially generated datasets with two explanatory variables for analysis and visualization. The methodology includes constructing and evaluating multilayer perceptrons with the R 'nnet' package. Additionally, an automatic procedure for determining the optimal number of hidden layer neurons is demonstrated, leveraging training and test data splits. The findings showcase the perceptron's effective supervised learning capabilities, particularly its ability to approximate complex functional relationships bet...
This document explains the role of hidden layer neurons in a perceptron using artificially generated data. It describes the dataset used, along with the defined decision boundaries for two classification problems. Finally, it outlines the process of implementing a multi-layer perceptron with the R package 'nnet' to determine the optimal number of hidden neurons.
This document focuses on the perceptron algorithm's application to a linearly separable dataset and a parity problem. In the first exercise, a dataset with binary targets defined by a linear boundary (y = x/2) is used for training, starting with weights initialized to zero. Examples are alternately presented until all are correctly classified. The second exercise extends the perceptron to classify parity using a seven-segment display of digits. Trace outputs highlight algorithmic iterations and updates during learning.
This document discusses the implementation of the K-Nearest Neighbors (K-NN) algorithm for classification, using the MNIST dataset, a labeled collection of handwritten digit images commonly used in machine learning for supervised learning tasks. The methodology involves data preprocessing, training/testing dataset splitting, visualization, and classifier training using Scikit-Learn's 'KNeighborsClassifier'. The K-NN model, when trained and tested, achieved a classification accuracy of approximately 98%. Additional tasks include experimenting with various values of K to compare classificatio...
This document outlines a lab project focused on implementing the K-Nearest Neighbors (K-NN) algorithm using the MNIST dataset. It provides objectives, references, and a hands-on approach to loading and visualizing handwritten digits. Students will apply the K-NN classifier to make predictions based on the dataset.
This document explores the application of linear discriminant analysis (LDA) using the famous Fisher's Iris dataset. The dataset, containing measurements of iris flowers, is analyzed with R software to classify flowers into three species—setosa, versicolor, and virginica—based on biometric features. LDA results demonstrate group means, discriminant coefficients, and visualization of species separation in the feature space. A predictive example calculates scores to classify a flower manually, complemented by similar steps implemented in Python.
This document discusses both descriptive and predictive discriminant analysis methodologies, focusing on separating groups of individuals based on quantitative explanatory variables and their qualitative classification. The approach maximizes inter-class variance and uses techniques such as Fisher's discriminant method and matrix calculations for optimal projection. Applications are demonstrated across domains like medicine, finance, marketing, and geology. Predictive analysis assigns new observations to classes based on proximity to calculated centroid regions.
This document presents the concept and applications of discriminant analysis in various fields, including medicine and finance. It describes how to separate individuals into classes based on qualitative and quantitative variables. Additionally, it discusses the methods for identifying discriminative variables and making predictions based on observations.
This document introduces the application of decision trees using the Gini Index as a measurement. A banking example is demonstrated, predicting client loan repayment success based on attribute selection. It explains step-by-step tree construction, detailizing rules extraction and key strengths such as interpretability, automatic variable selection, and robustness. Lastly, it discusses advantages like efficiency on medium datasets and drawbacks including instability on small datasets.