Examens - Intelligence artificielle et données

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.

The document outlines two exercises focusing on predictive modeling and statistical analysis. Exercise 1 requires constructing a decision tree using CART or Entropy for predicting company profitability and evaluating its performance. Exercise 2 involves applying simple linear regression to analyze the relationship between capital stock and company investment based on data from 27 industrial companies. The results from a data science software provide statistical estimates to assess regression quality and interpret findings.

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 outlines a two-part exam focused on designing and implementing a data warehouse and building an ETL process. The first part involves creating a new data warehouse schema, defining a time dimension table using Transact-SQL scripts, and populating it with dates for a specific year. The second part requires developing an ETL solution by restoring a database, analyzing its content to generate sample data and statistical reports, and performing data profiling tasks on key database tables. Key deliverables include multiple SQL scripts, output files, and profiling reports stored in sp...

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.