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Welcome to the homepage of the Machine Learning research group at the Institute of Computer Science, University of Tartu. Our main research topics are supervised learning (classifier adaptation, calibration, evaluation and ensembles) and applications of machine learning and deep learning for neuroscience, biology and health.

People

Academic staff:

Meelis Kull
Sven Laur
Leopold Parts
Raul Vicente Zafra

PhD students:

Mari-Liis Allikivi
Markus Kängsepp
Dmytro Fishman
Ilja Kuzovkin
Daniel Majoral

Master students in 2018/19:

Gunay Abdullayeva
Diana Grygorian
Anton Potapchuk
Hristijan Sardjoski
Novin Shahroudi
Prabhant Singh

Bachelor students in 2018/19:

Andre Litvin
Jaan Erik Pihel
Liina Anette Pärtel
Karl Riis

Master students in 2017/18:

Samreen Hassan
Theodore Heiser
Markus Kängsepp
Anton Potapchuk
Sten-Oliver Salumaa
Novin Shahroudi
Prabhant Singh
Martin Valgur
Brait Õispuu

Bachelor students in 2017/18:

Andreas Baum
Cardo Kambla
Joonas Kriisk
Laura Ruusmann
Egert Teesaar
Henry Teigar
Kristiina Uusna

 

Teaching

2019 Spring

Machine Learning II (6 ects) – lecturer: Sven Laur

Neural Networks (6 ects)- lecturer: Raul Vicente Zafra – teaching assistants: Ardi Tampuu, Roman Ring, Anti Ingel, Oriol Corcoll

Computational Neuroscience Seminar (3 ects) – instructors: Oriol Corcoll, Raul Vicente Zafra

Special Course in Machine Learning: Deep Reinforcement Learning (3 ects) – instructors: Tambet Matiisen, Roman Ring, Raul Vicente Zafra

Research Seminar in Data Mining (3 ects) – instructor: Sven Laur

2018 Fall

Introduction to Data Science (6 ects)- lecturer: Meelis Kull – teaching assistants: Mikk Puustusmaa

Machine Learning (6 ects) – lecturer: Meelis Kull – teaching assistants: Mikhail Papkov, Mikk Puustusmaa

Introduction to Computational Neuroscience (6 ects) – lecturer: Raul Vicente Zafra – teaching assistants: Aqeel Labash, Daniel Majoral Lopez

Computational Neuroscience Seminar (3 ects) – instructors: Oriol Corcoll, Raul Vicente Zafra

Special Course in Machine Learning: Deep Reinforcement Learning (3 ects) – instructors: Tambet Matiisen, Roman Ring, Raul Vicente Zafra

Research Seminar in Data Mining (3 ects) – instructor: Sven Laur

2018 Spring

Machine Learning (6 ects) – lecturer: Meelis Kull – teaching assistants: Mari-Liis Allikivi, Liis Kolberg

Introduction to Computational Neuroscience (6 ects) – lecturer: Raul Vicente Zafra – teaching assistants: Aqeel Labash, Daniel Majoral Lopez

Computational Neuroscience Seminar (3 ects) – instructor: Raul Vicente Zafra

Special Course in Machine Learning: M4 Regression Competition (3 ects) – instructor: Meelis Kull

Research Seminar in Data Mining (3 ects) – instructor: Sven Laur

2017 Fall

Data Mining (6 ects)- lecturer: Meelis Kull – teaching assistants: Mari-Liis Allikivi, Dmytro Fishman

Neural Networks (6 ects)- lecturer: Raul Vicente Zafra – teaching assistants: Tambet Matiisen, Ardi Tampuu

Computational Neuroscience Seminar (3 ects) – instructor: Raul Vicente Zafra

Special Course in Machine Learning: Ensemble methods (3 ects) – instructor: Meelis Kull

Research Seminar in Data Mining (3 ects) – instructor: Sven Laur

Selected publications

 

Non-parametric Bayesian Isotonic Calibration: Fighting Over-confidence in Binary Classification
M Allikivi, M Kull
Conference: ECML-PKDD’19 – The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases
2019

Shift Happens: Adjusting Classifiers
TJT Heiser, M Allikivi, M Kull
Conference: ECML-PKDD’19 – The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases
2019

Distribution calibration for regression
H Song, T Diethe, M Kull, P Flach
Conference: ICML’19 – The International Conference on Machine Learning
2019

Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex
I Kuzovkin, R Vicente, M Petton, J Lachaux, M Baciu, P Kahane, S Rheims, JR Vidal, J Aru
Journal: Communications Biology, bioRxiv, 133694
2018

Efficient neural decoding of self-location with a deep recurrent network
A Tampuu, T Matiisen, HF Ólafsdóttir, C Barry, and R Vicente
Journal: preprint (bioRxiv) 242867
2018

JACKS: joint analysis of CRISPR/Cas9 knock-out screens
F Allen, F Behan, F Iorio, K Yusa, M Garnett, L Parts
Journal: preprint (bioRxiv)
2018

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project
T Diethe, M Holmes, M Kull, M Perello Nieto, K Sokol, H Song, E Tonkin, N Twomey, P Flach
Conference: KDD Applied Data Science track
2018

Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning
T Pärnamaa, L Parts
Journal: G3: Genes, Genomes, Genetics
2017

Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
M Kull, TM Silva Filho, P Flach
Journal: Electronic Journal of Statistics
2017

Bivariate Partial Information Decomposition: The Optimization Perspective
M Abdullah, DO Theis, R Vicente
Journal: Entropy 19, no. 10, 530
2017

Computational biology: deep learning
W Jones, K Alasoo, D Fishman, L Parts
Journal: Emerging Topics in Life Sciences
2017

Linear Ensembles of Word Embedding Models.
A Muromägi, K Sirts, S Laur
Conference: NODALIDA (Nordic Conference on Computational Linguistics)
2017

Multiagent cooperation and competition with deep reinforcement learning
A Tampuu, T Matiisen, D Kodelja, I Kuzovkin, K Korjus, Juhan Aru, Jaan Aru, R Vicente
Journal: PloS one 12, no. 4: e0172395
2017

An efficient data partitioning to improve classification performance while keeping parameters interpretable
K Korjus, MN Hebart, R Vicente
Journal: PloS one 11, no. 8, e0161788.
2016

Cost-sensitive boosting algorithms: Do we really need them?
N Nikolaou, N Edakunni, M Kull, P Flach, G Brown
Journal: Machine Learning
2016 – ACM Computing Reviews named it as one of the most notable items published in computer science in 2016

Deep learning for computational biology
C Angermueller, T Pärnamaa, L Parts, O Stegle
Journal: Molecular Systems Biology
2016

EstNLTK – NLP Toolkit for Estonian
S Orasmaa, T Petmanson, A Tkachenko, S Laur, HJ Kaalep
Conference: LREC (International Conference on Language Resources and Evaluation)
2016

Predicting quantitative traits from genome and phenome with near perfect accuracy
K Märtens, J Hallin, J Warringer, G Liti, L Parts
Journal: Nature Communications
2016

Reframing in context: A systematic approach for model reuse in machine learning
J Hernández-Orallo, A Martínez-Usó, RBC Prudêncio, M Kull, P Flach, CF Ahmed, N Lachiche
Journal: AI Communications
2016

The SPHERE challenge: Activity recognition with multimodal sensor data
N Twomey, T Diethe, M Kull, H Song, M Camplani, S Hannuna, X Fafoutis, …
arXiv preprint arXiv:1603.00797
2016

Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Conference: NIPS’15 – Advances in Neural Information Processing Systems
2015

Privacy-Preserving Statistical Data Analysis on Federated Databases.
D Bogdanov, L Kamm, S Laur, P Pruulmann-Vengerfeldt, R Talviste, J Willemson
Conference: APF (Annual Privacy Forum)
2014

Reliability maps: a tool to enhance probability estimates and improve classification accuracy
M Kull, PA Flach
Conference: ECML-PKDD’14 – The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases – Best paper award
2014

A new way to protect privacy in large-scale genome-wide association studies.
L Kamm, D Bogdanov, S Laur, J Vilo
Journal: Bioinformatics
2013