The core of this course revolves around programming machine learning algorithms for yourself, as a way to truly understand what exactly they are learning. 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

Weekly on-site classes and self-study. In total, this will average around 8 hours per week. For the even weeks of the course there will be a seminar with mandatory attendance, while on the odd weeks there will be a tutorial session where you can ask questions and get help with your assignments.

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

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