Examens - Intelligence artificielle et données

This document is an academic exam covering Natural Language Processing (NLP) methods. It assesses Web scraping techniques using various Python libraries, and preprocesses text data by addressing issues like special characters and punctuations using NLTK and regex. Additionally, it explains representing data through the TF-IDF method to identify keyword relevance. Finally, it explores textual classification techniques, likely for categorization of textual datasets.

The document presents an examination structure focused on Natural Language Processing for a Master's level course. It includes tasks such as web scraping to detect false announcements, data preprocessing following the CRIS methodology, word embedding using TF-IDF, and text classification with the Naive Bayes algorithm. Methodology for web scraping, preprocessing collected datasets, calculating TF-IDF, and building a machine learning model for detecting false announcements is elaborated through practical sections.

This document provides a detailed exam for a Master's level course on Natural Language Processing, focusing on web scraping, data preprocessing, word embedding using TF-IDF, and text classification using the Naive Bayes algorithm. The exam consists of multiple parts addressing the collection of data, preprocessing techniques, representation of data, and classification methodologies.

This document consists of exam questions testing knowledge in deep learning concepts, architectures, and training methodologies. The questions cover topics like dataset splitting, evaluation metrics, overfitting prevention techniques, and convolutional neural networks. Each question assesses the understanding of fundamental principles as well as practical applications in machine learning.

This academic assessment consists of two major sections addressing key concepts and problem-solving methods in deep learning. Section 1 focuses on theoretical concepts such as overfitting, dataset splitting, and metrics for fatigue detection evaluation, alongside techniques like dropout and convolution options for model design and training. Section 2 emphasizes practical application, including convolution computations, neural network architecture analysis, and training methodologies with critical importance on data preprocessing. The test evaluates understanding of deep learning fundamental...

This document contains an exam focused on deep learning fundamentals with a specific emphasis on model training and evaluation techniques. It includes questions on dataset splitting, evaluation metrics, and neural network architectures. The document also poses practical problems requiring calculations, explanations, and identification of potential errors in code.

This document presents assessment items on Decision Support Systems focusing on data warehousing concepts like subject-oriented collections, integration, and data historicity. The methodology for practical application is illustrated via the design of a data warehouse schema for healthcare analytics, including primary keys, boolean attributes, and dimensional modeling. A separate practical scenario evaluates vehicle rental contracts using financial metrics to ensure solution scalability and operational service levels. Core calculations about storage dimensions and performance are included to...

This document contains a detailed Big Data exam focusing on Apache Spark, Hadoop, Hive, and Pig. It evaluates knowledge of Spark APIs, functionalities, and components, alongside Hadoop architecture and Hive capabilities. The exam covers practical aspects of data processing, querying systems, and programming paradigms in Big Data frameworks. Core methodologies include in-memory computations, lazy evaluations, and using specific tools for tasks like transformations, data analysis, and SQL integration.

This document evaluates knowledge on probabilistic classification methods including majority decision rules, maximum likelihood, and Bayes rule. It involves analyzing a dataset with patients divided into two classes (healthy and sick) based on binary attributes (blood pressure and cholesterol). Key calculations include determining probabilities for specific cases and developing classification rules for one or two attributes. The exam also emphasizes clarity in responses and detailed explanations for methodology and results.

The document outlines a discriminant analysis conducted on a dataset of 46 companies (23 healthy and 23 failing) using financial ratios. Fisher's linear discriminant functions are applied to predict company classification based on ratios, presenting both equations for healthy and failing companies. A predictive example identifies a company's classification based on ratios. A second exercise demonstrates decision tree classification for qualitative observation data using the Gini Index criterion, including tree construction, rule extraction, and method evaluation.

The document is an exam for a Master's program in Business Analytics and Data Science at the Université Virtuelle de Tunis, primarily covering Machine Learning. It consists of problems focusing on the differentiation of data mining methods, discriminant analysis, and predictive modeling for employee retention. Additionally, it examines regression analyses on business investment, interpreting parameters and improving models using explanatory variables. The exam applies analytical techniques and mathematical formulations such as ordinary least squares and decision trees.

This document is the exam for the Machine Learning course as part of the Master's program in Business Analytics and Data Science at the Université Virtuelle de Tunis. It comprises various exercises evaluating theoretical knowledge and practical applications related to data mining and classification methods. Key topics include the differences between descriptive and predictive methods, classification techniques from artificial intelligence, and regression analysis.

This document is an examination covering foundational and advanced concepts in Big Data Analytics and Deep Learning. The Big Data section focuses on tools and frameworks such as Hadoop, Hive, Sqoop, Flume, and Spark, emphasizing technical commands, configurations, and comparisons. The Deep Learning section assesses conceptual understanding of neural networks, activation functions, architectures like CNN and RNN, training methodologies like gradient descent, and applications such as autoencoders and hyperparameter tuning. Methodologies of data processing, model optimization, and their advant...

The exam tests knowledge across key concepts in big data analytics, including an understanding of Hadoop 2 architecture, Elasticsearch clusters, and the use of tools such as Kibana, Flume, and Sqoop. Additionally, the exam evaluates practical skills in Spark programming, focusing on RDD manipulation, transformations, and actions, with comparison to newer paradigms like DataFrames. Lastly, it includes a question section on machine learning basics, covering supervised and unsupervised learning, as well as distinctions between classification and regression methodologies.

This document is an exam for a Big Data module with exercises covering data storage in Hive, persistence under HDFS, troubleshooting Pig scripts, designing MapReduce workflows, and multiple-choice questions on Hadoop concepts. The exercises require understanding Hadoop's components, Pig Latin scripts, and Hive queries, alongside the mechanisms for data replication and system efficiency improvements introduced by YARN. It emphasizes practical implementation, conceptual understanding, and ecosystem utilities.

This document is an academic exam covering foundational and advanced knowledge in big data technologies, including HDFS, Hive, Spark, Flume, and Sqoop. It evaluates students' understanding of file operations in HDFS, database management with Hive, and the integration of data pipelines using Apache tools. The questions assess students' technical skills in command usage, system design, resource allocation, and code analysis. The exam emphasizes distributed computing concepts, data storage, and real-life use cases of big data frameworks.

The exam assesses foundational knowledge of Big Data, focusing on Hadoop and related technologies. It covers concepts such as Name Node failures, relational database limitations, and NoSQL database types. The assessment also includes HDFS replication advantages, CAP theorem properties, and Big Data's core attributes of volume, variety, and velocity. Lastly, it evaluates understanding of distributed file systems, and specific tools like Pig and Hive.

The document provides detailed responses to an old exam focusing on discriminant analysis and decision trees. It includes a study of company data categorized as healthy or failing. The analysis includes calculation of discriminant functions and predictions based on financial ratios.

This document presents two exercises focusing on predictive modeling and statistical analysis. In the first exercise, the algorithm for decision tree construction is described, using CART criteria based on Gini Index or entropy, and discussing its advantages and drawbacks. The quality of the predictive model is evaluated. The second exercise analyzes business investment (Y) in relation to capital stock (X) through simple linear regression. The statistical outputs are interpreted and used to assess regression quality and validate hypotheses.

This document outlines the competition for entrance into engineering training cycles in Tunisia. It includes details about the mathematics and physics examination, along with a focus on a computer science problem related to supervised machine learning. The exam consists of problems designed to test the implementation of binary tree structures for predictive modeling.

The document is a supervised exam focused on distributed systems and their application in big data. It evaluates the understanding of vector clocks, matrix clocks, and token-based mutual exclusion in distributed systems. The exercise requires calculating time synchronization within systems, resolving mutual exclusion scenarios, and schematizing critical section access sequences based on specific conditions. The methodologies demonstrate a combination of theoretical models and practical problem-solving in distributed environments.

The document focuses on analyzing distributed systems using vector clocks and matrix clocks for synchronization. It includes exercises to calculate dependencies and mutual exclusion scenarios with token-based control. Additionally, critical section entry sequences are modeled under different configurations in a distributed setup.

The document contains two exercises focusing on distributed systems. The first exercise involves calculating vector and matrix clocks to track chronological events in the given distributed system. The second exercise addresses mutual exclusion using a token-based control mechanism for a distributed system with four sites, modeling scenarios for ordering critical section access based on token availability and requests.

This document presents a statistical analysis exercise split into two parts. The first focuses on performing one-way ANOVA tests on decathlon event data to determine competition-based differences in the 100m and javelin events. The second part analyzes 16 years of financial indicators from an oil company, explaining how to construct a data set for Principal Component Analysis (PCA) in SPSS. It identifies variables and observations, evaluates inertia distribution on axes, and explores correlations and patterns in financial data. Key findings include strong negative correlations between EXP a...