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.
 

• 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

Mode of study: Weekly on-site classes and self-study. In total, this will average around 8 hours per week.

On-site classes: Every Friday from 14:00-16:00. 

Every module consists of 2 or 3 weeks. On even weeks there will be a seminar with mandatory attendance (see below for dates). On odd weeks this will be an optional tutorial  to ask questions about the material you may have, and to get help with assignments.    

We also offer an on-site optional kick-off / installation session on 27 September 2024 if you need help to install the software on your computer.  

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).  

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