This post contains a list of the AI-related seminars that are scheduled to take place between 5 May and 30 June 2025. All events detailed here are free and open for anyone to attend virtually. Gurobi Machine Learning Speaker: Roland Wunderling (Gurobi Optimisation) Organised by: Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list. Beyond Returns: A Candlestick-Based Approach to Covariance Estimation Speaker: Yasin Simsek (Duke University) Organised by: Statistics and Machine Learning in Finance, University of Oxford Join the mailing list to receive notifications about the seminar series. Robust and Conjugate Gaussian Processes Regression Speaker: François-Xavier Briol (University College London) Organised by: Finnish Center for Artificial Intelligence Zoom link is here.

This post contains a list of the AI-related seminars that are scheduled to take place between 1 April and 31 May 2025. All events detailed here are free and open for anyone to attend virtually. Lie-Poisson Neural Networks (LPNets): Data-Based Computing of Hamiltonian Systems Speaker: Vakhtang Poutkaradze (University of Alberta) Organised by: University of Minnesota Zoom registration is here. Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function. Speaker: Anh Nguyen (Carnegie Mellon University) Organised by: Carnegie Mellon University Zoom link is here.

This post contains a list of the AI-related seminars that are scheduled to take place between 3 February and 31 March 2025. All events detailed here are free and open for anyone to attend virtually. Concept bottleneck language models for protein design Speakers: Aya Abdelsalam, PhD (Guide Labs) & Nathan Frey, PhD (Prescient Design) Organised by: ML Protein Engineering Sign up to the mailing list for instructions on how to join (scroll to the end of the page). Bridging smooth regression and mathematical optimization Speaker: Vanesa Guerrero (Universidad Carlos III de Madrid) Organised by: Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list. Misinformation and Social Media as a Historical Process: Insights from the American Experience Speaker: James W. Cortada Organised by: The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here.

AI is a broad and rapidly developing field of technology. Our goal is to make sure all young people have the skills, knowledge, and confidence to use and create AI systems. So what should AI education in schools look like? To hear a range of insights into this, we organised a panel discussion as part of our seminar series on AI and data science education, which we co-host with The Alan Turing Institute. You can also watch the recording below.

A recent Forbes article reported that over the last four years, the use of artificial intelligence (AI) tools in many business sectors has grown by 270%. AI has a history dating back to Alan Turing's work in the 1940s, and we can define AI as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. Four key areas of AI are machine learning, robotics, computer vision, and natural language processing. Other advances in computing technology mean we can now store and efficiently analyse colossal amounts of data (big data); consequently, data science was formed as an interdisciplinary field combining mathematics, statistics, and computer science. Data science is often presented as intertwined with machine learning, as data scientists commonly use machine learning techniques in their analysis.

Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. In ths seminar series we ask distinguished speakers to comment on what role Bayesian statistics and Bayesian machine learning have in this rapidly changing landscape. Do we need to optimally process information or borrow strength in the big data era? Are philosophical concepts such as coherence and the likelihood principle relevant when you are running a large scale recommender system?

Artificial intelligence offers healthcare professionals a powerful tool for … "Machine Learning, Image Analysis, and Imaging in Atherosclerotic …

We have recently launched an ongoing online seminar series about Bayesian machine learning as scale. The intended audience includes machine learning practitioners and statisticians from academia and industry. Registration is now open for Jake Hofman's 17 June talk: "How visualizing inferential uncertainty can mislead readers about treatment effects in scientific results". Jake is a Senior Principal Researcher at Microsoft Research, New York. The talk is at 15.00 UTC this Wednesday, June 17; to see it in your local time zone please go to the registration page.

ECMWF is organising a series of seminars given by international experts to explore aspects of the use of machine learning in weather prediction and climate studies. The first will take place on 28 April and will be live-streamed. Sherman Lo and Ritabrata Dutta from the University of Warwick will present a statistical methodology to predict precipitation at 0.1 resolution using lower-resolution model fields of air temperature, geopotential, specific humidity, total column water vapour and wind velocity. On 9 June, Annalisa Bracco from the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology will talk about spatiotemporal complexity and time-dependent networks in mid- to late Holocene simulations. In subsequent seminars, Maxime Taillardat (Météo-France) will present examples of operational ensemble post-processing using machine learning; Alberto Arribas (UK Met Office) will talk about work at the Met Office Informatics Lab; and Nal Kalchbrenner (Google) will talk about now-casting applications at Google.

This seminar series is the first formal gathering of academic and industrial data scientists across the Greater Toronto Area (GTA) to discuss advanced topics in machine learning and its goal is to build a stronger machine learning community in Toronto. The talks will be given by international and local faculty and industry professionals. The seminar series is intended for university faculty and graduate students in machine learning across computer science, ECE, statistics, mathematics, linguistics, and medicine, as well as PhD-level data scientists doing interesting applied research in the GTA. A large emphasis will be placed on the social aspects of the gathering. The Toronto machine learning community will be stronger when we know each other and know what problems people are working on.