Advances and challenges in machine learning languages


The development of machine learning programming languages is critical to support the research and deployment of ML solutions as data-size and model-complexity grow. This workshop aims to bring together researchers from both academia and industry, to discuss recent advances and challenges in machine learning languages development and research.

Dates Monday, May 20, 2019 - Download as vCalendar
Venue Centre For Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA
Email coordinator@bigdata.cam.ac.uk
Website Event website

Event details:

The development of machine learning programming languages is critical to support the research and deployment of ML solutions as data-size and model-complexity grow. These languages often offer built-in support for expressing machine learning models as programs and aim at automating inference, through probabilistic analysis and simulation or back-propagation and differentiation. Machine learning languages enable models to be deployed, critiqued, and improved, support reproducible research, and lower the barrier for the use of these methods.

This workshop aims to bring together researchers from both academia and industry, to discuss recent advances and challenges in machine learning languages development and research.

Registration

Academic Attendees

Registration for Academic Attendees is FREE but essential as places are limited. Please complete the online Registration HERE.

Industry and Other Non-Academic Partners

There is a Registration Fee of £80 (reduced to £40 for SMEs) for Industry Partners.  The Registration Fee includes admission to all seminars, copy of the programme, lunch, refreshments and a wine reception. Other meals or accommodation are not included.

Once you have completed your online Registration HERE, please complete your Payment HERE and email a copy of your payment confirmation or reference number to coordinator@bigdata.cam.ac.uk.

The Cambridge Big Data Strategic Research Initiative brings together researchers from across the University to address challenges presented by our access to unprecedented volumes of data.