LxMLS 2020 will take place July 21st to July 29th at Instituto Superior Técnico (IST), a leading Engineering and Science school in Portugal. It is organized jointly by IST, the Instituto de Telecomunicações, the Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa (INESC-ID), Unbabel, Priberam Labs and Cleverly
Click here for information about past editions (LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019) and to watch the videos of the lectures (2016, 2017,2018).
Due to the current COVID-19 situation LxMLS 2020 will be held as a fully virtual on-line school on the same dates (July 21st – 29th) and the same schedule with the expected limitations imposed by time-zones. We are excited for the opportunity to create a virtual school, where you will be able to attend all the lectures, and participate Q&As and labs remotely. We will also provide the tools for students to engage which each other remotely. The lectures will also be streamed to YouTube, and latter they will become freely available in our YouTube channel (see also the schedule for the slides). The Q&A, labs and social activities will remain restricted to the accepted students only.
Our target audience is:
- Researchers and graduate students in the fields of NLP and Computational Linguistics;
- Computer scientists who have interests in statistics and machine learning;
- Industry practitioners who desire a more in depth understanding of these subjects.
Features of LxMLS:
- No deep previous knowledge of ML or NLP is required, but the attendants are assumed to have some basic background on mathematics and programming
- Lecturers are leading researchers in machine learning and natural language processing (see speakers)
- Days are divided into morning lectures and afternoon lab sessions and practical talks (see schedule)
- The Labs guide will be provided one month in advance. This year’s guide can be found here
- A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises
- Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning, reinforcement learning) will be covered
A group photo from previous year’s attendees:
A group photo previous year’s labs team: