About this Event
Hosted by the Liverpool Advanced Methods for Big Data Analytics (LAMBDA) Research Cluster, this workshop aims to explore cutting-edge methodologies rooted in Machine Learning and High-Dimensional Analysis, with the goal of extracting meaningful patterns and insights from datasets characterised by a large number of variables and/or observations.
Given the increasing complexity and dimensionality of modern data, these methodologies primarily focus on designing algorithms capable of effectively handling high-dimensional information, particularly prevalent in fields such as finance, economics, marketing, etc.
The objectives of the workshop encompass refining machine learning models to enhance predictive accuracy and interpretability in high-dimensional settings.
Researchers aim to address challenges related to model overfitting, feature selection, and computational efficiency, ultimately advancing the understanding and application of machine learning techniques in scenarios where traditional methods may be limited.
The overarching goal is to contribute to the development of robust and efficient tools for improved decision-making.
Academics, students, alumni, business leaders and policymakers, with a special emphasis on the relevance for PhD students in social sciences are welcome to join the event.
Participating in this workshop offers a unique opportunity to discover first-hand insights from experts from academia and the latest research developments in this area.
Keynote speakers
- , Professor at the IDS Department (Information Systems, Decision Sciences and Statistics) at ESSEC Business School
- , Professor of Economics, University of Oxford
- , Professor of Econometrics, University of Surrey
- , Professor at the IDS Department (Information Systems, Decision Sciences and Statistics) at ESSEC Business School ESSEC Business School
Event Venue & Nearby Stays
University of Liverpool School of Law & Social Justice, University of Liverpool School of Law & Social Justice, Liverpool, United Kingdom
GBP 0.00