LxMLS 2017 will take place July 20-27 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, and Priberam Labs.
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 at least some basic background on probabilities and programming;
- Recommended reading is provided one month in advance, see the labs guide of 2016 as reference;
Includes a strong practical component;
- A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises;
- Days will be divided into tutorials and practical sessions (see schedule);
- Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning) will be covered;
- Instructors are leading researchers in machine learning.
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 at least some basic background on probabilities and programming;
- Recommended reading is provided one month in advance, see the labs guide of 2016 as reference;
Includes a strong practical component;
- A day zero is scheduled to review basic concepts and introduce the necessary tools for implementation exercises;
- Days will be divided into tutorials and practical sessions (see schedule);
- Both basic (e.g linear classifiers) and advanced topics (e.g. deep learning) will be covered;
- Instructors are leading researchers in machine learning.