addressalign-toparrow-leftarrow-rightbackbellblockcalendarcameraccwcheckchevron-downchevron-leftchevron-rightchevron-small-downchevron-small-leftchevron-small-rightchevron-small-upchevron-upcircle-with-checkcircle-with-crosscircle-with-pluscrossdots-three-verticaleditemptyheartexporteye-with-lineeyefacebookfolderfullheartglobegmailgooglegroupsimageimagesinstagramlinklocation-pinm-swarmSearchmailmessagesminusmoremuplabelShape 3 + Rectangle 1outlookpersonJoin Group on CardStartprice-ribbonShapeShapeShapeImported LayersImported LayersImported Layersshieldstartickettrashtriangle-downtriangle-uptwitteruserwarningyahoo

Trustworthy ML and Generating Music Traditions

  • 6 days ago · 6:30 PM
  • AHL

Agenda is the same as usual:

- 18:30:  doors open, pizza, beer, networking

- 19:00: First talk

- 20:00: Break & networking

- 20:15: Second talk

- 21:30: Close

The value of evaluation: towards trustworthy machine learning - Peter Flach

Machine learning, broadly defined as data-driven technology to enhance human decision making, is already in widespread use and will soon be ubiquitous and indispensable in all areas of human endeavour. Data is collected routinely in all areas of significant societal relevance including law, policy, national security, education and healthcare, and machine learning informs decision making by detecting patterns in the data. Achieving transparency, robustness and trustworthiness of these machine learning applications is hence of paramount importance, and evaluation procedures and metrics play a key role in this.

In this talk I will review current issues in theory and practice of evaluating predictive machine learning models. Many issues arise from a limited appreciation of the importance of the scale on which metrics are expressed. I will discuss why it is OK to use the arithmetic average for aggregating accuracies achieved over different test sets but not for aggregating F-scores. I will also discuss why it is OK to use logistic scaling to calibrate the scores of a support vector machine but not to calibrate naive Bayes. More generally, I will discuss the need for a dedicated measurement theory for machine learning that would use latent-variable models such as item-response theory from psychometrics in order to estimate latent skills and capabilities from observable traits.

Bio: Peter Flach has been Professor of Artificial Intelligence at the University of Bristol since 2003. An internationally leading researcher in the areas of mining highly structured data and the evaluation and improvement of machine learning models using ROC analysis, he has also published on the logic and philosophy of machine learning, and on the combination of logic and probability. He is author of Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012).

Prof Flach is the Editor-in-Chief of the Machine Learning journal, one of the two top journals in the field that has been published for over 25 years by Kluwer and now Springer. He was Programme Co-Chair of the 1999 International Conference on Inductive Logic Programming, the 2001 European Conference on Machine Learning, the 2009 ACM Conference on Knowledge Discovery and Data Mining, and the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases in Bristol.

Working toward computer generated music traditions - Bob Sturm

I will discuss research aimed at making computers intelligent and sensitive enough to working with music data, whether acoustic or symbolic. Invariably, this includes _a lot_ of work in applying machine learning to music collections in order to divine distinguishing and identifiable characteristics of practices that defy strict definition. Many of the resulting machine music listening systems appear to be musically sensitive and intelligent, but their fraudulent ways can be revealed when they are used to create music in the styles they have been taught to identify. Such "evaluation by generation” is a powerful way to gauge the generality of what a machine has learned to do. I will present several examples, focusing in particular on our work applying deep LSTM networks to modelling folk music transcriptions, and ultimately generating new music traditions.

Bio: Bob is currently a Lecturer in Digital Media at the Centre for Digital Music ( in the School of Electronic Engineering and Computer Science, Queen Mary University of London. He is a founding member of the Machine Listening Lab (, and belongs to the Applied Machine Learning Lab ( He specialises in audio and music signal processing, machine listening, and evaluation. In September 2016, he organised the HORSE2016 workshop at QMUL: . Stay tuned for HORSE2017!

Join or login to comment.

Our Sponsors

People in this
Meetup are also in:

Sign up

Meetup members, Log in

By clicking "Sign up" or "Sign up using Facebook", you confirm that you accept our Terms of Service & Privacy Policy