Machine Learning, Maths & Ethics: Hands-on (POK)

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

Learning Schedule

Machine Learning, Maths & Ethics: Hands-on is structured in five weeks plus one week for introduction.
Week 0 – Welcome and introduction
Week 1 – Learning from experience: Machine learning and supervised learning
Week 2 – How we are going to work in supervised learning models
Week 3 – Data preparation, data exploration and statistics
Week 4 – Training models, evaluating models and matrices
Week 5 – Ethical challenges of machine learning algorithms

Throughout these topics you will learn:

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