Medicine stands apart from other areas where machine learning (ML) can be applied. Where we have seen advances in other fields driven by lots of data, it is the complexity of medicine, not the volume of data, that makes the challenge so hard. But at the same time this makes medicine the most exciting area for anyone who is really interested in exploring the boundaries of ML, because we are given real-world problems to formalize and solve. And the solutions are ones that are societally important, and they potentially impact us all (just think COVID-19!).

ML has of course already achieved very impressive results in numerous areas. Standout examples include computer vision and image recognition, playing games or in teaching robots. AI empowered by ML is so good at mastering these things because they are easily-stated problems where the solutions are well-defined and easily verifiable. “Easily-stated problems” have a clear challenge to solve and clear rules to play by; “well-defined solutions,” fall into a easily recognizable class of answers; while a “verifiable solution” is one that we as humans can understand in terms of judging whether the model has succeeded or not. Unfortunately, in medicine the problems are not well-posed, the solutions are often not well-defined, and they aren’t easy to verify.

This tutorial will present new methods to build clinical decision support systems at scale, forecast disease trajectories, estimate individualized treatment effects, personalize active monitoring and screening, and transfer knowledge across clinical environments. It will also discuss how to make ML interpretable, explainable and trustworthy so that clinicians, patients and policy makers can use it to derive actionable intelligence. Finally, it will discuss how all of these technologies can be integrated to build learning machines for healthcare, transforming electronic health records that are simply capturing data into engines of personalized decision support, cooperation, effective health management and discovery.

Many ML tasks share practical goals and theoretical foundations with signal processing (consider, e.g., spectral and kernel methods, differential equation systems, sequential sampling techniques, and control theory). Signal processing methods are an integral part of many sub-fields in ML, with links to, for example, Reinforcement learning, Hamiltonian Monte Carlo, Gaussian process (GP) models, Bayesian optimization, and neural ODEs/SDEs.

This tutorials aims to cover aspects in machine learning that link to both discrete-time and continuous-time signal processing methods. Special focus is put on introducing stochastic differential equations (SDEs), state space models, and recursive estimation (Bayesian filtering and smoothing) for Gaussian process models. The goals are to (i) teach basic principles of direct links between signal processing and machine learning, (ii) provide an intuitive hands-on understanding of what stochastic differential equations are all about, (iii) show how these methods have real benefits in speeding up learning, improving inference, and model building—with illustrative and practical application examples. This is to show how ML can leverage existing theory to improve and accelerate research, and to provide a unifying overview to the ICML community members working in the intersection of these methods.

The field of representation learning without labels, also known as unsupervised or self-supervised learning, is seeing significant progress. New techniques have been put forward that approach or even exceed the performance of fully supervised techniques in large-scale and competitive benchmarks such as image classification, while also showing improvements in label-efficiency by multiple orders of magnitude. Representation learning without labels is therefore finally starting to address some of the major challenges in modern deep learning. To continue making progress, however, it is important to systematically understand the nature of the learnt representations and the learning objectives that give rise to them.

In this tutorial we will: - Provide a unifying overview of the state of the art in representation learning without labels, - Contextualise these methods through a number of theoretical lenses, including generative modelling, manifold learning and causality, - Argue for the importance of careful and systematic evaluation of representations and provide an overview of the pros and cons of current evaluation methods.