L48 Machine Learning and the Physical World

Aims

The module “Machine Learning and the Physical World” 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 scares, 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.

There are three principle points about machine learning in the real world that will concern us.

  1. We often have a mechanistic understanding of the real world which we should be able to bootstrap to make decisions. For example, equations from physics or an understanding of economics.
  2. Real world decisions have consequences which may have costs, and often these cost functions need to be assimilated into our machine learning system.
  3. The real world is surprising, it does things that you do not expect and accounting for these challenges requires us to build more robust and or interpretable systems.

Decision making in the real world hasn’t begun only with the advent of machine learning technologies. There are other domains which take these areas seriously, physics, environmental scientists, econometricians, statisticians, operational researchers. This course identifies how machine learning can contribute and become a tool within these fields. It will equip you with an understanding of methodologies based on uncertainty and decision making functions for delivering on these challenges.

Objectives

You will gain detailed knowledge of

  • surrogate models and uncertainty
  • surrogate-based optimization
  • sensitivity analysis
  • experimental design

You will gain knowledge of

  • counterfactual analysis
  • surrogate-based quadrature

* Lectures take place in the Department of Computer Science and Technology and are part of the MPhil in Advanced Computer Science.