Data Mining & Machine Learning

CPE Lyon

Academic Year 2025-2026

Intelligent data analysis through machine learning algorithms, pattern recognition, neural networks, natural language processing, and semantic technologies to build predictive models and extract actionable insights.
TRAINING DATA ML MODEL ML F1 F2 PREDICTIONS Class A 72% Class B 28% feedback ACCURACY 80% Σ ∇ θ INTELLIGENT LEARNING

Course Overview

Syllabus

Course Content & Structure

Complete syllabus outlining all topics covered in this course.

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Course Materials

Introduction

Introduction

Comprehensive overview of learning objectives, assessment criteria, and detailed schedule of lectures and practical sessions throughout the academic year.

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Data Processing

Data Representation & Analysis

Techniques for data preprocessing, extraction, transformation, analysis, and visualization.

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Mining Algorithms

Patterns & Algorithms

Pattern recognition, classification, clustering, association rules, and fundamental data mining algorithms.

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Neural Networks

Neural Networks & Ethics

Artificial neural networks architecture, training algorithms, ethical considerations in ML, and responsible AI practices.

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Semantic Web

Linked Open Data

Semantic web technologies, RDF, SPARQL, and linked data for intelligent systems.

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NLP

Natural Language Processing

Text processing, sentiment analysis, named entity recognition, and NLP applications in ML.

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Practical Sessions

GitHub Repository

DataMining Repository

Comprehensive collection of ML and data mining exercises implementing classification, clustering, neural networks, NLP projects, and real-world datasets. Complete with model training scripts, evaluation tools, and comprehensive tutorials in French.

Access on GitHub

Past Exam Questions

2018-2019

English

French

2017-2018

English

French