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A meaningful learning opportunity to empower young people, especially young women to pursue a career in machine learning through a gender conscious narrative.

Course description

Would you like to know the basics of machine learning, but don't know where to start? Would you like to learn about the societal challenges of machine learning? Then, this course is for you!

This hands-on course in Machine Learning, Maths & Ethics, teaches you about the foundations of Machine Learning in an intuitive way. It has a heavy focus on exercises and examples of its applications. The course allows you to develop practical skills to build algorithms and stimulate critical thinking on the ethics of machine learning models.

Although the fields of computer science, artificial intelligence and machine learning are changing the world, the truth is that girls and women are still underrepresented in these fields. We prepared this online course, following the guidelines of FOSTWOM Erasmus+ project to develop MOOCs according to a gender conscious perspective in narratives, in the language, and in the use of images. Thus, we expect this MOOC to be a meaningful learning opportunity to empower young people, especially young women to follow these areas of expertise.

Are you going to miss the opportunity to learn about a technology that is transforming the world?

Total workload of the course: 40 hours

This MOOC has been designed by Instituto Superior Técnico (Lisbon), provided by Politecnico di Milano and developed in the frame of the FOSTWOM project.

This MOOC is one of the outputs of the Erasmus+ FOSTWOM project (No.2019-1-ES01-KA203-065924).

European Union

Co-funded by the Erasmus+ Programme of the European Union

The European Commission’s support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Intended Learning Outcomes

Throughout these topics you will learn:

  1. What machine learning is;
    ESCO: principles of artificial intelligence
  2. The different types of machine learning, and supervised learning in more detail;
    ESCO: machine learning
  3. The standard process of building predictive models;
    ESCO: algorithms
  4. The four steps of the standard process: data preparation, data exploration, model training, model evaluation;
    ESCO: algorithms
  5. Some fundamental Maths needed to understand machine learning: Statistics and Linear Algebra;
    ESCO: statistics
  6. How to program in Python with Google Colab;
    ESCO: ML (computer programming)
  7. How to be aware of the challenges of building fair machine learning algorithms.
    ESCO: follow ethical code of conduct


No prerequisite knowledge is required for this course.


Over and above consulting the content, in the form of videos and other web-based resources, you will have the opportunity to discuss course topics and to share ideas with your peers in the Forum of this MOOC.

Topic outline

  • Week 0 - Introduction to the course

    Not available unless: You are a(n) Student
  • Week 1 - Learning from experience

    Not available unless: You are a(n) Student
  • Week 2 - How we are going to work

    Not available unless: You are a(n) Student
  • Week 3 - Data preparation and data exploration

    Not available unless: You are a(n) Student
  • Week 4 - Training models and evaluating models

    Not available unless: You are a(n) Student
  • Week 5 - Why we should care

    Not available unless: You are a(n) Student
  • Additional Resources

    • Folder icon
      Video transcript Folder
      Not available unless: You are a(n) Student


The final grade for the course is based on your results from your responses to the graded quizzes. You will successfully complete the course if you reach 60% (or more) of the total score. The maximum score possible for each quiz is given at the top of the quiz. You can see your score in the quiz on your last attempt or on the 'Grades' page.

Certificate of accomplishment

You must be registered in POK through Politecnico di Milano personal account to obtain the Certificate of Accomplishment. It will be released to anyone who successfully completed the course by achieving at least 60% of the total score in the graded quizzes and filling the final survey

You will be able to download the Certificate of Accomplishment directly from Politecnico di Milano web services.

The Certificate of Accomplishment does not confer any academic credit, grade or degree. 

Information about fees and access to materials

You can access the course absolutely free of charge and completely online.

Course faculty

Pedro Marcelino

Pedro Marcelino


Co-founder of TreeTree2.

PhD in “A New Approach for the Maintenance Management of Transport Infrastructures using Machine Learning” at Instituto Superior Técnico.

Masters degree in Civil Engineering, Structures’ branch, at Instituto Superior Técnico.

Ana Moura Santos

Ana Moura Santos


Received her diploma in Physics-Mathematics Sciences from the University of Moscow, and the MSc and PhD degrees in Applied Mathematics from Técnico, where she started teaching in 1987, first at the Department of Physics and, from 1993 on, at the department of Mathematics.

The area of her research is Operator Theory and Functional Analysis with applications, and also works on pedagogical issues, namely developing e-learning resources for projects in Mathematics.

She spends most of her free time in activities related to dance, presently practicing sevillanas and flamenco.

Contact details

If you have any enquiries about the course or if you need technical assistance please contact For further information, see FAQ page.