The core of this course revolves around programming machine learning algorithms for yourself, as a way to truly understand what exactly they are learning. The course has 4 modules in total. Each of the different modules will focus on programming a different algorithm, understanding the math required for that algorithm, and discussing a philosophical question or a societal impact related to applying this algorithm in practice.
Specifically, we’ll cover the following algorithms:

• k-Nearest Neighbours
• Naive Bayes
• Gradient Descent
• (Multivariate) Linear Regression
• Polynomial Regression

Note: This explicitly does not include Neural Networks, as that is too large a topic to also include here. However, the foundational concepts covered are also all applied within Neural Networks, and so this does provide the necessary basis to study Neural Networks in a follow-up course.
 

Every module consists of 2 or 3 weeks.

Every module has one or two sessions with mandatory attendance. In total there are 5 on-site mandatory sessions scheduled on Wednesdays from 14:00-16:00. Online participation is not possible. 

On the weeks where there are no scheduled lectures, participants can come and ask questions during the Q&A sessions. There will be several timeslots scheduled for Q&A sessions every day those weeks, so participants may choose whichever option fits their schedule best. The exact schedule for these optional Q&A sessions will be published at a later date.

We also offer an on-site optional kick-off / installation session before the start of the course, if you need help to install the software on your computer.  

In total, the course will average around 8 hours per week.

The exact schedule of the sessions is shown in the table below.

• 4 programming assignments
• 4 writing assignments
• a final exam

• Know the main types of  machine learning algorithms, and when to apply each
• Know how to train a simple machine learning model using your own data set
• Know how to detect underfitting or overfitting, and select the best model complexity for a task
• Understand the foundational mathematics that allow all ML algorithms to learn from large amounts of data
• Understand some of the important societal risks when applying ML in practice
• Understand what it means for an algorithm to “learn” something, and the capabilities and limitations this implies

Study material: The study materials included in the course consist of reading materials, theory videos, and optional self-study modules for mathematics.

Laptop: You will need to bring your own laptop to program on for the assignments (make sure you have rights to install software on the device).