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Machine Learning in Marketing

Objectives

1. Introducing the students to the concept of learning and automatic learning, and related issues
2. Familiarizing the students with the main Machine Learning problems: regression, forecasting, classification, clustering
3. Familiarizing the students with the main measures of performance of a Machine Learning model
4. Introducing the most used Machine Learning algorithms
5. Applying those algorithms to marketing

General characterization

Code

200203

Credits

7.5

Responsible teacher

Docente a designar

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

  

Bibliography

- Andrew Ng. Machine Lear ning Yearning. Online Book: http://www.mlye arning.org. 2017.
- Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from Data. AMLBook. 2012.

Teaching method

  

Evaluation method

  

Subject matter

1. Introduction to learning and Machine Learning
2. Generalization and Overfitting
3. Supervised and Unsupervised Learning
4. Classification, clustering, regression, forecasting
5. Measures of performance of a Machine Learning model
6. Supervised Neural Networks
7. Unsupervised Neural Networks
8. Deep Learning
9. Genetic Programming
10. Hints to Support Vect or Machines and Bayesian Networks
11. Applications to real-life test cases in the area of Marketing