Aprendizagem Automática em Marketing

Objetivos

Machine learning is a discipline within the field of Artificial Intelligence, which, through algorithms and statistics, provides computers with 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

Código

200203

Créditos

7.5

Professor responsável

Nuno Miguel da Conceição António

Horas

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

Pré-requisitos

It is recommended that students have basic knowledge of statistics, Python, and data science (explore and clean data).

Bibliografia

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 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

Método de avaliação

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 4
- 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.

Conteúdo

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

Cursos

Cursos onde a unidade curricular é leccionada: