Conference starts in:

Early-bird submissions in:

Find a new job

You are here

22.08.2016 13:15–16:00 Other event Exactum Auditoriums A111 and B123
22.05.2017 14:00–16:00 Discussion event CK112/Exactum The Faculty of Science has established a new MSc programme in Data Science. The programme will start next autumn, with the aim of educating new generations of data science experts and professionals for various fields of science as well as for companies and administration.
12.01.2017 13:00–13:30 Lecture Exactum B222 Dr. Pan Hui is a candidate for a Professor position in Data Science and gives a teaching demonstration under title "Introduction to Instruction Pipelining Execution in Microprocessors" on Thursday 12.1.2017 at 13.00 in room C222 B222, Exactum.  
18.09.2017 09:15–10:00 HIIT seminar Exactum D122, Kumpula Elja Arjas, Professor Emeritus of Mathematics and Statistics, University of Helsinki Probabilistic Preference Learning With The Mallows Rank Model
27.02.2017 09:00–10:00 HIIT seminar Exactum D123, Kumpula This week's speaker at our Machine Learning Coffee seminar will be
20.02.2017 09:15–10:00 HIIT seminar Konemiehentie 2, seminar room T5 This week's speaker at the Machine Learning Coffee seminar is Alexander Jung.
13.02.2017 09:15–10:00 HIIT seminar Exactum D123, Kumpula This week's speaker at our Machine Learning Coffee seminar will be
06.02.2017 09:15–10:00 HIIT seminar Konemiehentie 2, seminar room T5 The Machine Learning Coffee seminar (now featuring delicious porridge as well!) will continue on Monday, February 6th in Otaniemi.
03.02.2017 15:15–16:30 Guest lecture Small Hall, Main Building, University of Helsinki, Fabianinkatu 33, Helsinki We have great pleasure to invite you to a talk by MIT Professor Rosalind Picard. As part of the Helsinki Distinguished Lecture Series on Future Information Technology, Professor Picard will talk about building emotional intelligence technologies. (https://www.hiit.fi/HelsinkiITLectures)
30.01.2017 09:00–10:00 HIIT seminar Exactum D123, Kumpula Likelihood-free Inference and Predictions for Computational Epidemiology

Pages