Nikolaos (Nikos) Nikolaou

Selected Presentations

The list below is outdated. For paper presentations, go to the publications page.


2018 - 2019


Correcting Transiting Exoplanet Light Curves for Stellar Spots: The 1st ARIEL Machine Learning Competition
A description of the machine learning challenge I organised in the context of the upcoming ESA M4 Ariel Space Mission. The challenge was hosted by the European Conference on Machine Learning (ECML-PKDD 2019). The talk discusses the problem, the motivation, the data, organizational aspects and the very promising initial baseline solutions our team developped. It was presented in the UK Exoplanets Meeting 2019 (UKEXOM 2019), London, UK. An earlier version of the talk was given in the ARIEL Space Mission Consortium in Palermo (March 2019). Stay posted for the update in the European Week of Astronomy & Space Science 2019, Lyon, France (June 2019).


AI Techniques for Exoplanets (Part 1 & Part 2)
A lecture for the Exoplanets postgraduate course of the Department of Physics and Astronomy of the University College London, UK. It is meant as a gentle introduction to Artificial Intelligence and in particular Machine Learning and its applications in the field of Exoplanetary Science.


Margin Maximization as Lossless Maximal Compression
My latest research on the theoretical side of machine learning, connecting hypothesis margin maximization in classification to information theory. Slides to be uploaded soon. Presented in the Greek Stochastics 2018 meeting in Athens, Greece and in the School of Mathematics, Statistics and Actuarial Science of the University of Kent, UK.


Introduction to Machine Learning
Introductory talk, the first of the Machine Learning PAD seminar series of the Department of Physics & Astronomy of the University College London, UK.

2016 - 2017


Learning from Imbalanced Classes: Problem Statement & Methods
Talk contributed to Workshop on Class Imbalance in Machine Learning Classification, organized by the 4IR STFC Centre for Doctoral Training in Data Intensive Science of the University of Manchester, UK. An updated version was delivered in February 2019 as part of the Machine Learning PAD seminar series of the Department of Physics & Astronomy of the University College London, UK.


Boosting for Probability Estimation & Cost-Sensitive Learning
Lecture on the work I carried out during my PhD, along with online extensions and connections to other areas of machine learning I am currently exploring. This is the full version of the talk (~ 1.5h). Appropriately adapted, shorter versions were delivered in:

2015

Asymmetric boosting algorithms: Do we really need them?
Lecture on my latest research delivered in the Research Symposium of the University of Manchester, UK.

Cost-sensitive learning with AdaBoost
A brief introduction to cost-sensitive learning, followed by my latest research on cost-sensitive AdaBoost, delivered to the postgraduate class of COMP61011: Foundations of Machine Learning of the University of Manchester, UK.

Optimal inductive inference and its approximations
Two-part talk on inductive inference delivered to the members of the Machine Learning and Optimization research group of the University of Manchester, UK. We first gave an intuitive interpretation of Solomonoff Induction, an intractable formalization of optimal inductive inference which combines ideas from philosophy, computer science, statistics & information theory. We then saw how nature and machine learning overcome the ill-posedness of induction by introducing assumptions and settling for approximations. Concepts covered included: common assumptions, bias-variance tradeoff, inductive bias & the no-free-lunch theorems, the role of Occam's Razor and elements of statistical learning theory.

2014

Introduction to AdaBoost
Introductory lecture on AdaBoost delivered to the postgraduate class of COMP61011: Foundations of Machine Learning of the University of Manchester, UK.