University of Tartu Institute of Computer Science is organising data science seminars to a wider audience where researchers, lecturers, students, alumni and industry representatives are sharing their knowledge on the subject. The seminars are conducted in English. Seminars are recorded and can be viewed afterwards.
The IT Academy Research Summit was a two-day event packed with highlight talks, poster sessions, free discussions, and speed-dating opportunities. You can watch here the recording of the plenary sessions.
The seminar was moderated by Prof. Jaak Vilo. The event featured the following speakers:
The seminar recording can be found here. The photos are available
The seminar was moderated by Marlon Dumas. The seminar featured the following speakers:
You can find the seminar recording here.
The seminar featured the following speakers:
You can find the seminar recording here.
The speaker Raivo Kolde has been supported by European Social Fund via „ICT programme“ measure.
Seeing is believing and humans rely on their sight to make decisions. As a result, a lot of data we gather in various ways is visual. It is natural to want to get the most out of the data we have and one method toward achieving this is semantic segmentation. In essence, semantic segmentation means figuring out which pixels belong to which type of object. For example, how can a machine recognize a pedestrian on a road crossing or differentiate between fields and swamplands? Deep Learning has helped make huge strides in the field of semantic segmentation but there are still many challenges to overcome!
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You can find the seminar recording here.
The benefits arising from Artificial Intelligence (AI) in terms of prediction accuracy, automation, new products and services or cost reduction are remarkable. But enterprises need to build trust and transparency in the data and algorithm used in AI systems to increase adoption. By default, AI systems such as machine learning or deep learning produce outputs with no explanation or context. As the predicted outcomes turn into recommendations, decisions or direct actions, humans tend to look for justification. Most experts in the field agree that AI systems should be less ambiguous to the end-users and subjects of algorithmic decision-making. In this seminar, four speakers from the University of Tartu and Wise will discuss the explainability and transparency of AI as well as the requirements and challenges of building a robust machine learning system.
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You can find the seminar recording here.
Moderators:
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Moderator: Mark Fišel (UniTartuCS)
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Moderator: Riccardo Tommasini, Lecturer of Data Management at UniTartuCS
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Moderator: Ülar Allas from High Performance Computing Center of UniTartu
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Moderator: Elena Sügis (UniTartuCS)
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(Online)
Introduction to the topic by:
Kaur Alasoo & Riccardo Tommasini (University of Tartu Institute of Computer Science)
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You can find the seminar recording here.
(Online)
Moderator: Jaak Vilo (UniTartu ICS)
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You can find the seminar recording here.
Moderator: Tambet Matiisen (UniTartu Autonomous Driving Lab)
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Moderator: Alexander Nolte (UniTartu ICS)
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Moderator: Meelis Kull (UniTartu ICS)
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Moderator: Meelis Kull (UniTartu ICS)
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Moderator: Sherif Sakr (UniTartu ICS)
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Moderator: Amnir Hadachi (UniTartu ICS)
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Moderator: Leopold Parts (UniTartu ICS)
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Moderator: Mark Fišel (UniTartu ICS)
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Moderator: Leopold Parts (UniTartu ICS)
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You can find the seminar recording here.
Moderator: Tambet Matiisen (UniTartu ICS)
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Moderator: Dmytro Fishman (UniTartu ICS)
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Moderator: Raul Vicente (UniTartu ICS)
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Moderator: Meelis Kull (UniTartu ICS)
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Moderator: Leopold Parts (UniTartu ICS)
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Moderator: Leopold Parts (UniTartu ICS)
Speakers and presentations:
You can find the seminar recording here.
Moderator: Kristjan Eljand (STACC)
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You can find the seminar recording here.
Moderator: Leopold Parts (UniTartu ICS)
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