Welcome to the course Machine Learning and the Physical World. The course is focused on machine learning systems that interact directly with the real world. Building artificial systems that interact with the physical world have significantly different challenges compared to the purely digital domain. In the real world data is scarce, often uncertain and decisions can have costly and irreversible consequences. However, we also have the benefit of centuries of scientific knowledge that we can draw from. This module will provide the methodological background to machine learning applied in this scenario. We will study how we can build models with a principled treatment of uncertainty, allowing us to leverage prior knowledge and provide decisions that can be interrogated.


Details of the course lectures can be found here.


Details of the course practical sessions can be found here.

Case Studies

The lectures for weeks 6 to 8 will focus on "special topics". The lectures will be given by guest lectures discussing a specific real world scenario where they have applied machine learning or machine learning would be applicable. In session 2 of week 8, we will discuss the material in light of the course and discuss potential challenges that could provide the basis for a project.

Details of the course case studies can be found here.