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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 ensembles, uncertainty estimation) and applications of machine learning and deep learning for neuroscience, biology, health and autonomous driving.

People

Academic staff:

Meelis Kull
Sven Laur
Leopold Parts
Raul Vicente Zafra

PhD students:

Mohammed Ali
Mari-Liis Allikivi
Dmytro Fishman
Viacheslav Komisarenko
Ilja Kuzovkin
Markus Kängsepp
Tõnis Laasfeld
Daniel Majoral
Mikhail Papkov
Novin Shahroudi

Alumni (including past master and bachelor students)

Teaching

2019 Fall

Introduction to Data Science (6 ects)- lecturer: Meelis Kull – teaching assistants: Markus Kängsepp, Laura Ruusmann

Machine Learning (6 ects) – lecturer: Meelis Kull – teaching assistants: Mohamed Abdelrahman, Vladyslav Fediukov, Viacheslav Komisarenko, Mikhail Papkov, Novin Shahroudi

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: Fast.ai (3 ects) – instructors: Dmytro Fishman, Tambet Matiisen

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

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

Past teaching (since 2017 Fall)

Industrial collaborations

Current:

PerkinElmer (since 2017)
Bolt (since 2019)

Selected publications

 

Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
M Kull, MP Nieto, M Kängsepp, T Silva Filho, H Song, P Flach
Conference: NeurIPS’19 – Advances in Neural Information Processing Systems
2019

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

Segmenting nuclei in brightfield images with neural networks
D Fishman, S Salumaa, D Majoral, S Peel, J Wildenhain, A Schreiner, K Palo, L Parts
Preprint: bioRxiv, https://www.biorxiv.org/content/early/2019/09/10/764894
2019

JACKS: joint analysis of CRISPR/Cas9 knockout screens
F Allen, F Behan, A Khodak, F Iorio, K Yusa, M Garnett, L Parts
Journal: Genome research
2019

Predicting the mutations generated by repair of Cas9-induced double-strand breaks
F Allen, L Crepaldi, C Alsinet, AJ Strong, V Kleshchevnikov,
P De Angeli, P Páleníková, A Khodak, V Kiselev , M Kosicki,
AR Bassett , H Harding, Y Galanty, F Muñoz-Martínez,
E Metzakopian, SP Jackson, L Parts
Journal: Nature Biotechnology
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
Preprint: bioRxiv 242867
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

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data
D Moradigaravand, M Palm, A Farewell, V Mustonen, J Warringer, L Parts
Journal: PLoS computational biology
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