The brain remains the only known example of a truly general-purpose intelligent system. The study of human and animal cognition has revealed key insights, such as the ideas of parallel distributed processing, biological vision, and learning from reward signals, that have heavily influenced the design of artificial learning systems. Many AI researchers continue to look to neuroscience as a source of inspiration and insight. A key difficulty is that neuroscience is a vast and heterogeneous area of study, encompassing a bewildering array of subfields. In this tutorial, we will seek to provide both a broad overview of neuroscience as a whole, as well as a focused look at two areas -- computational cognitive neuroscience and the neuroscience of learning in circuits -- that we believe are particularly relevant for AI researchers today. We will conclude by highlighting several ongoing lines of work that seek to import insights from these areas of neuroscience into AI, and vice versa.

Bayesian probabilistic modelling provides a principled framework for coherent inference and prediction under uncertainty. Approximate inference addresses the key challenge of Bayesian computation, that is, the computation of the intractable posterior distribution and related quantities such as the Bayesian predictive distribution. Significant progress has been made in this field during the past 10 years, which enables a wide application of Bayesian modelling techniques to machine learning tasks in computer vision, natural language processing, reinforcement learning etc.

This tutorial offers a coherent summary of the recent advances in approximate inference. We will start the tutorial with an introduction to the approximate inference concept and the basics in variational inference. Then we will describe the fundamental aspects of the modern approximate inference, including scalable inference, Monte Carlo techniques, amortized inference, approximate posterior design, and optimisation objectives. The connections between these recent advances will also be discussed. Lastly, we will provide application examples of advanced approximate inference techniques to downstream uncertainty estimation and decision-making tasks and conclude with a discussion on future research directions.

Timetable Tutorial part 1: basics of approximate inference (approx. 30min) Coffee break & live Q&A 1 (approx. 10min) Tutorial part 2: advances 1 (approx. 30min) Coffee break & live Q&A 2 (approx. 10min) Tutorial part 3: advances 2 (approx. 30min) Coffee break & live Q&A 3 (approx. 10min) Tutorial part 3: applications (approx. 30min)