Examens - Intelligence artificielle et données

This document outlines an exam focusing on decision trees and linear regression. It includes exercises on decision tree algorithms and the application of linear regression using real data. Students are tasked with evaluating methods and interpreting statistical results.

The document discusses a decision-support system module with a focus on data warehouse design for the Tunisian Postal Service. The theoretical portion includes comparisons of data warehouse architectures and scenarios where implementing a data warehouse is unsuitable. A case study emphasizes designing a data warehouse for payment tracking via smart payment cards. The proposed data model needs to support comprehensive reporting on financial metrics such as revenue by provider or zone, client segmentation by transaction behaviors, and promotional campaign targeting.

This document outlines an examination for a module on Decision Support Systems at the Université Virtuelle de Tunis. It includes reflective questions and a case study focusing on the implementation of a Data Warehouse for the Tunisian Post, emphasizing the monitoring of payments through various services. The examination assesses students' understanding of specific concepts and their ability to model information systems.

This document details a Data Warehousing exam administered by the Université Virtuelle de Tunis. It covers two exercises: (1) designing a star schema and a snowflake schema to improve sales data collection and analysis for a chain of magazine stores, with specific analytical indicators and SQL queries for sales analysis; (2) creating star and snowflake schemas for an academic institution to understand factors affecting student exam success, including demographic and course-specific performance data. The document emphasizes the importance of dimensional modeling and schema transformations fo...

This document contains an exam for the Master 1 program in Computer Science at Université Paris 7, focusing on Artificial Intelligence. The exam consists of exercises related to search algorithms and heuristics, including A* search and alpha-beta pruning techniques. Students are required to analyze heuristics for admissibility and dominance, as well as apply search strategies to find optimal paths in given scenarios.

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.