Statistical learning refers to a vast set of tools and techniques for modelling and understanding complex datasets. A great deal of interest on these aspects has recently surged in the statistical and computer science communities, and statistical learning has become a very hot field in many applications, such as marketing, finance, and other business disciplines. In this short introduction we...
Intelligent systems are ubiquitous. Their growth and deployment are addressing industrial, scientific and societal challenges. A brief overview of the applications of intelligent systems and predictive models - with focus on current and future opportunities - is offered. A major focus is given on scientific challenges in medical sciences, chemistry, bioinformatics, geosciences and physics.
What do we mean by "data and compute infrastructures" and to what extent they can be considered "Intelligent"? How and why are these terms changed over the past 20 years? This lecture will introduce the main results and challenges for efficient exploitation of data-driven research.
The speaker will present the state of the art in data science, the role of (big) data in it, the skills needed to perform efficiently as a data scientist, and the key concepts around data, algorithms and techniques in the field.
The speaker will give an overview of the utilisation of ML/DL techniques in High Energy Physics, with and without accelerators, giving (selected) examples. The talk will present the impact of such techniques in triggering, tracking and reconstruction, end-user data analysis, computing infrastructure optimization, etc. The talk will also discuss the transition from the use of field-specific...
Choosing a proper level of complexity for a prediction rule (model selection) or evaluating its performance (model assessment) are two fundamental steps in any supervised statistical learning application. Both steps require reliable estimates of the expected prediction error. After providing some general definitions related to the prediction error in regression and classification, attention...
