Aprendizagem Automática em Marketing


Machine learning is a discipline within the field of Artificial Intelligence, which, through algorithms and statistics, provides computers the ability to identify patterns in data and make predictions. This curricular unit will familiarize students with the most common machine learning algorithms and their applications in marketing.

Caracterização geral





Professor responsável

Nuno Miguel da Conceição António


Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês


It is recommended that students have basic knowledge of statistics and Python.


Método de ensino

The curricular unit is based on theoretical-practical classes. The sessions include the presentation of concepts and methodologies and the practical application of different concepts using different languages and computer applications. Several teaching strategies are applied, including slides presentation and step-by-step instructions on approaching practical examples, questions, and answers. The practical component is oriented towards exploring tools introduced to students, including the discussion of the best approach in different scenarios.

Applications used: Python, Jupyter notebook, Microsoft visual code, Azure Machine Learning Studio (classic).

Método de avaliação

Due to the application-based design of the course, evaluation is continuous.

All evaluation grades are on a scale of 0-20. The final course grade is calculated based on the following weights:

  • Completion of self-assessment survey: 2.5%
  • Group membership submission (in the due deadline): 2.5%
  • Group project (minimum grade is 8.0):
    • Materials (datasets, code, etc.) and report: 55%
    • Due date: to be confirmed (around 10 days before 2nd season exam date)
  • Exam (individual - with materials consultation - minimum grade is 8.0): 40%

All submissions should be made via Moodle. Submissions after the deadline will be rejected.


  • LU1. Introduction to Machine Learning, Data Mining, and Predictive Analytics.
  • LU2. Machine Learning applications in marketing.
  • LU3. Machine Learning libraries for Python and introduction to AZMLS.
  • LU4. CRoss-Industry Standard Process for Data Mining (CRISP-DM) Methodology.
  • LU5. Data understanding.
  • LU6. Data preparation.
  • LU7. Model validation, generalization, and overfitting.
  • LU8. Supervised learning - regression: performance measures.
  • LU9. Main families of algorithms: linear regression, decision trees, neural networks, Support Vector. Machines (SVM), K-Nearest Neighbors (KNN).
  • LU10. Supervised learning - classification: performance measures.
  • LU11. Main families of algorithms: logistic regression, decision trees, neural networks, Naive Bayes, SVM, and KNN.
  • LU12. Ensembles of methods.
  • LU13. Unsupervised learning - clustering: K-Means algorithm.
  • LU14. Application in marketing cases.