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Data Mining and Machine Learning

  • Enseignant(s):   M.Vlachos  
  • Titre en français: Data mining et méthodes d'apprentissage
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre d'automne 2020-2021, 4.0h. de cours (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formation concernée: Maîtrise universitaire ès Sciences en systèmes d'information

[attention] Le syllabus du cours est entrain d'être modifié par le professeur responsable. Veuillez consulter cette page à nouveau dans quelques jours. --- A titre informatif uniquement, voici l'ancien syllabus :


Today, enterprises collect troves of data about their clients: historical purchases, responses to marketing events, web search logs, etc. In today’s data-driven economy, data can assist us in better understanding our customers, and in taking more informed decisions about our business.

Some of questions that we will answer in this class are:

  1. How do we represent different types of data: text, time-series, images, high-dimensional data?
  2. How can we search efficiently across this data?
  3. How do we extract useful and actionable information from this data to augment the value of our business?
  4. How do we perform exploratory data analysis and how can we effectively visualize data?
  5. How can we build and deploy predictive models?

Goal: Understand the terminology of data science and machine learning, comprehend the potential pitfalls, get a general understanding of how to address real-world problems using Python code. You will also learn how to create and deploy APIs for the tools that you will build.


A sample of the topics that we will cover in the course include:

  1. Introduction: Data Mining and Machine Learning, Concepts and Terminology. Applications: Targeted Marketing, and Customer Modeling
  2. Data Preparation for Knowledge Discovery, Exploratory Data Analysis and Visualization
  3. Regression and Logistic Regression
  4. Classification: Decision Trees and Nearest Neighbor Classification
  5. Evaluation of the predictive model
  6. Neural Networks
  7. Text Analytics
  8. Association rules
  9. Recommender Systems, Chatbots
  10. Search and indexing of data
  11. Clustering (kMeans, Hierarchical Clustering)
  12. Dimensionality reduction, PCA, Time-Series

You have to bring your laptop in every class, as we will do hands-on exercises and quizzes.


- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Foster Provost, Tom Fawcett, ISBN-13: 978-1449361327

- Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Second Edition, 2005, ISBN: 0-12-088407-0


- Good knowledge of Python and object-oriented-programming (OOP) in Python



1ère tentative

Sans examen (cf. modalités)  

  • In-class quizzes.
  • Personal programming assignments in Python.
  • A group project in Python.
  • Class participation.



Sans examen (cf. modalités)  

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