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.

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done.

Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package.

This course is about statistical analysis of vector data and machine learning using vector data. In this course I demonstrate open source python packages for the analysis of vector-based geospatial data. I use Jupyter Notebooks as an interactive Python environment. GeoPandas is used for reading and storing geospatial data, exploratory data analysis, preparing data for use in statistical models (feature engineering, dealing with outlier and missing data, etc.), and simple plotting. Statsmodels is used for statistical inference as it provides more detail on the explanatory power of individual explanatory variables and a framework for model selection.

A recent debate on so called Foundation models (CRFM) brings a real question of if we can build very large models on any specified domain, similar to current large language models, and replace our ...

Checkout this book that is practical and beginner-friendly and focuses on implement statistical and ML methods in Python. It goes over: Statistics, Machine Learning Methods and Algorithms, and some Deep Learning all in Python!

An applicant who has received a Degree of Doctor or has the equivalent academic expertise shall be qualified for this appointment. Priority shall be given to a person who has been awarded a doctoral degree or achieved equivalent academic expertise no more than five years before the deadline for applications for employment as assistant professor. A person who has been awarded a doctoral degree or has achieved equivalent expertise at a previous date may, however, be considered in special circumstances. Special circumstances is here used to describe: sick leave, parental leave, and other similar circumstances. Grounds for assessment As grounds for assessment when appointing an assistant professor, the level of proficiency required to qualify for the appointment shall apply.

Data scientists naturally use a lot of machine learning algorithms, which work well for detecting patterns, automating simple tasks, generalizing responses and other data heavy tasks. As a subfield of computer science, machine learning evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Over time, machine learning has borrowed from many other fields, including statistics. Most of today's algorithms have a history in various mathematical subfields. Many of these subfields overlap but I've taken a stab at categorizing some popular algorithms.