Machine Learning


The curricular unit of machine learning has as primary goal to allow students to gain a fundamental understanding of the art and science of Predictive Analytics as it relates to improving business performance.
This course will cover the basics of predictive analytics and modeling data to determine which algorithms to use. Understand the similarities and differences and which options affect the models most.
Topics covered include predictive analytics algorithms for supervised learning, including decision trees, neural networks, k-nearest neighbor, support vector machines, and model ensembles.
At the end of the course, participants will be able to use these skills to produce a fully processed data set compatible with building powerful predictive models that can be deployed to increase profitability.

General characterization





Responsible teacher

Roberto André Pereira Henriques


Weekly - Available soon

Total - Available soon

Teaching language

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


No requirements are needed


Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component includes exercises to consolidate the theoretical concepts covered in the theoretical classes.

Evaluation method

Regular period (1st examination period)

  • Exam (50%)
  • Final Project (50%)

Regular period (end examination period)

  • Exam (50%)
  • Final Project (50%)

Minimum grade of 8.0 (in 20) for the exam

Minimum grade of 4.0 (in 20) for the projects

Subject matter

LU1.    Introduction to machine learning
LU2.    Data transformation and preprocessing
LU3.    Model Selection and evaluation
LU4.    Introduction to classifiers
a.    Linear and Logistic regression
b.    Probability based learning
c.    Similarity based learning
d.    Regression and classification trees
e.    Ensemble classifiers
f.    Neural networks
g.    Support Vector Machines



Programs where the course is taught: