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Machine Learning in Marketing

Objectives

Machine learning is a discipline within the field of artificial intelligence that, through algorithms and statistics, allows computers 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.

General characterization

Code

200203

Credits

7.5

Responsible teacher

Ana Isabel Neves Edmundo

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Completion of one of the following courses: Data Science for Marketing, Descriptive Analytics in Marketing, or Descriptive Methods of Data Mining.

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

Bibliography

Teaching method

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 slide presentations 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

Evaluation method

Due to the application-based design of the course, evaluation is continuous and applies to both the theory and practical components. There is no “one only exam” with a single weight of 100%.

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

  • Three group projects:
    • Members: 3 to 5
    • Delivery for each project: Python notebook (commented)
    • Weight on final grade: 20% per project
  • Exam:
    • Individual
    • With consultation of materials
    • Minimum grade: 8.0:
    • Weight on final grade: 40%

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

Subject matter

LU1. Introduction to Machine Learning
LU2. Machine Learning applications in marketing.
LU3. CRISP-DM for Machine Learning.
LU4. Model validation, generalization, and overfitting.
LU5. Supervised learning - regression: performance measures.
LU6. Main families of algorithms: linear regression, decision trees, neural networks, Support Vector Machines (SVM), K-Nearest Neighbors (KNN).
LU7. Supervised learning - classification: performance measures.
LU8. Main families of algorithms: logistic regression, decision trees, neural networks, Naive Bayes, SVM, and KNN.
LU9. Ensembles of methods.
LU10. Unsupervised learning - clustering.
LU11. Implementing Machine Learning projects.