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 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.

The document outlines exam questions and exercises for a course on decision support systems. It includes theoretical questions comparing OLTP and OLAP databases, the modeling of data warehouses, and the differentiation between slow and fast-changing dimensions. Practical exercises involve designing data warehouse schemas for a toy manufacturer and a telecom operator, incorporating star and snowflake schema designs, and addressing specific business queries on sales, customer behavior, and TV audience analytics. The methodology emphasizes the application of dimensional modeling techniques for...

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 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 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...

The document discusses decision tree learning as a predictive modeling technique commonly used in statistics, data mining, and machine learning. It explains the decision tree structure, with branches representing observations and leaves representing conclusions about target values. The strengths highlighted include intelligible knowledge representation and automatic variable selection, while weaknesses point to stability issues with small datasets and challenges in detecting variable interactions. The document emphasizes decision tree robustness and efficiency for medium-sized datasets.

This document presents an examination on designing a decision tree model to predict customer interest in purchasing a product. Various factors such as gender, age, marital status, and income level are taken into account. The task involves constructing the decision tree using entropy or Gini index, converting it into a set of learning rules, and analyzing the strengths and weaknesses of the decision tree methodology. The document emphasizes practical application and assessment of decision tree learning techniques.

The document outlines a supervised exam focusing on decision tree methodology used to predict customer interest in a product based on features such as gender, age, marital status, and income level. Students are instructed to construct a decision tree using a chosen criterion (entropy or Gini index) and then convert it into a rule-based learning system. Additionally, the task involves discussing the advantages and limitations of decision tree methods. The exam emphasizes practical application of concepts in Machine Learning and the manipulation of algorithms for predictive modeling.

This document is an exam focused on machine learning concepts, specifically on building a decision tree model. It covers evaluating customer interest in purchasing a product based on various demographic factors. Students are required to demonstrate their understanding of decision trees, criteria choice, and provide insights into the methodology used.