Machine Learning (Aprendizado de Máquina)

Pós-graduação em Ciência da Computação

Instituto de Computação, Universidade Federal Fluminense.

Campus Praia Vermelha, Niterói.

As the 2016.1 semester is over I am updating this page in preparation for 2016.2. Bear in mind that the content is being modified.

Syllabus (Ementa)

The course provides an introduction to the foundations of machine learning. We will discuss the following topics:

  • Basic machine learning concepts;
  • classes of problems;
  • data representation and attributes;
  • supervised, unsupervised semi-supervised and reinforcement;
  • regression algorithms;
  • classification algorithms;
  • clustering algorithms;
  • generalization, bias/variance dilemma, overfitting;
  • performance assessment.

Class organization

The course is supported by a set of Jupyter/IPython notebooks.

You can also retrieve the notebooks from GitHub and run them locally for a better experience by cloning the git repository.

Similarly, you can use binder to run the notebooks online. In this case, bear in mind that all the changes you make can be lost.

Students must enroll on the class online LMS at Whenever possible use piazza for all communications. If not, include TIC10021 in the subject of your emails.

Please give yourself a profile picture – this will be really useful for all of us to learn each other’s names.


Reading list

This reading list contains papers that we have commented or discussed in class but that are not necessarily essential to the course. Feel free to contribute to the list via the Mendeley group.

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Questions and doubts

Students can book a meeting with the professor using the form bellow.