I have been teaching on courses related to artificial intelligence, machine learning, statistics, signal processing, software tools, and mathematics. During my basic studies, I served as a TA on courses in first-year engineering mathematics, which then gradually turned into teaching on more advanced courses in statistical signal processing during my doctoral studies.
As a faculty member at Aalto University, I have been responsible for designing and lecturing the course ‘Introduction to Artificial Intelligence’ which is intended as a primer in AI/ML and open for all students in the university (including students in arts and business).
I have also been invited to give conference tutorials and lectures in summer schools, most notably the ICML 2020 3-hour tutorial Machine Learning with Signal Processing and the Gaussian Process Summer School 2019.
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.