LxMLS 2014 will take place July 22-29 at Instituto Superior Técnico, a leading Engineering and Science school in Portugal. It is organized jointly by IST, the Instituto de Telecomunicações and the Spoken Language Systems Lab – L2F of INESC-ID. Click here for information about past editions (LxMLS 2011, LxMLS 2012, LxMLS 2013) and to watch the videos of the lectures (2011, 2012, 2013).
In our fourth edition, the topic of the school is Learning With Big Data.
The school covers a range of machine learning (ML) Topics, from theory to practice, that are important in solving natural language processing (NLP) problems that arise in the analysis and use of Web data.
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 assumed;
Recommended reading is provided in advance;
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 (view schedule);
Both basic and advanced topics are covered;
Instructors are leading researchers in machine learning.
In our fourth edition, the topic of the school is Learning With Big Data.
The school covers a range of machine learning (ML) Topics, from theory to practice, that are important in solving natural language processing (NLP) problems that arise in the analysis and use of Web data.
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 assumed;
Recommended reading is provided in advance;
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 (view schedule);
Both basic and advanced topics are covered;
Instructors are leading researchers in machine learning.