Machine Learning research group | Institute of Computer Science, University of Tartu

2022 Fall

Introduction to Data Science (6 ects)- lecturer: Meelis Kull – teaching assistants: Victor Pinheiro, Anna Aljanaki, Markus Kängsepp, Ingvar Baranin, Friedrich Krull

Andmeteaduse meetodid (Methods in Data Science, in Estonian) (6 ects)- lecturer: Meelis Kull – teaching assistants: Joonas Järve, Carel Kuusk, Marilin Moor

Machine Learning (6 ects) – lecturer: Dmytro Fishman – teaching assistants: Lisa Yankovskaya, Victor Pinheiro, Pavel Chizhov, Joonas Ariva, Mohammed Ali

Introduction to Computational Neuroscience (6 ects) – lecturer: Raul Vicente Zafra – teaching assistants: Marharyta Domnich, Farid Hasanov, Kristjan Kaup

Computational Neuroscience Seminar (3 ects) – instructors: Raul Vicente Zafra, Aqeel Labash, Marharyta Domnich

Special Course in Machine Learning: Probabilistic Deep Learning (3 ects) – instructors: Sven Laur

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

2022 Spring

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

Neural Networks (6 ects)- lecturer: Raul Vicente Zafra – teaching assistants: Marharyta Domnich, Mari-Liis Allikivi, Tarun Khajuria, Victor Pinheiro

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

Special Course in Machine Learning: Uncertainty in Machine Learning
(3 ects) – instructors: Meelis Kull

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

2021 Fall

Introduction to Data Science (6 ects)- lecturer: Meelis Kull – teaching assistants: Victor Pinheiro, Anna Aljanaki, Markus Kängsepp, Farid Hasanov, Friedrich Krull

Andmeteaduse meetodid (Methods in Data Science, in Estonian) (6 ects)- lecturer: Meelis Kull – teaching assistants: Sander Tamm, Carel Kuusk, Simo Sirel

Machine Learning (6 ects) – lecturer: Dmytro Fishman – teaching assistants: Mohammed Ali, Victor Pinheiro, Tetiana Shtym, Tetiana Rabiichuk, Taavi Luik

Introduction to Computational Neuroscience (6 ects) – lecturer: Raul Vicente Zafra – teaching assistants: Marharyta Domnich, Farid Hasanov, Kristjan Kaup

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

Special Course in Machine Learning: Geometric Deep Learning (3 ects) – instructors: Raul Vicente, Florian Stelzer, Kallol Roy

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

Past teaching (since 2017 Fall)

Quantifying Reinforcement-Learning Agent’s Autonomy, Reliance on Memory and Internalisation of the Environment.
A Ingel, A Makkeh, O Corcoll, R Vicente
Journal: Entropy
2022

Instance-based Label Smoothing For Better Calibrated Classification Networks
M Maher, M Kull
Conference: ICMLA’21 – Proceedings of the 20th IEEE International Conference on Machine Learning and Applications
2021

Information Bottleneck as Optimisation Method for SSVEP-Based BCI
A Ingel, R Vicente
Journal: Frontiers in Human Neuroscience
2021

Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops
F Stelzer, A Röhm, R Vicente, I Fischer, S Yanchuk
Journal: Nature communications
2021

A granular recurrent neural network for multiple time series prediction
S Tomasiello, V Loia, A Khaliq
Journal: Neural Computing and Applications
2021

Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images
MAS Ali, O Misko, SO Salumaa, M Papkov, K Palo, D Fishman, L Parts
Journal: SLAS DISCOVERY: Advancing the Science of Drug Discovery
2021

Correlated daily time series and forecasting in the M4 competition
A Ingel, N Shahroudi, M Kängsepp, A Tättar, V Komisarenko, M Kull
Journal: International Journal of Forecasting
2020

A Survey of End-to-End Driving: Architectures and Training Methods
A Tampuu, T Matiisen, M Semikin, D Fishman, N Muhammad
Journal: IEEE Transactions on Neural Networks and Learning Systems
2020

Perspective Taking in Deep Reinforcement Learning Agents
A Labash, J Aru, T Matiisen, A Tampuu, R Vicente
Journal: Frontiers in Computational Neuroscience
2020

A model for time interval learning in the Purkinje cell
D Majoral, A Zemmar, R Vicente
Journal: PLoS Computational Biology
2020

On a granular functional link network for classification
F Colace, V Loia, W Pedrycz, S Tomasiello
Journal: Neurocomputing
2020

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

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
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

Self-regulated learning with approximate reasoning and situation awareness
G D’Aniello, A Gaeta, M Gaeta, S Tomasiello
Journal: Journal of Ambient Intelligence and Humanized Computing
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

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

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

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

Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
M Kull, P Flach
Conference: ECML’15 – Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2015

Versatile decision trees for learning over multiple contexts
R Al-Otaibi, RBC Prudêncio, M Kull, P Flach
Conference: ECML’15 – Joint European Conference on Machine Learning and Knowledge Discovery in Databases
2015

Reframing in frequent pattern mining
CF Ahmed, M Samiullah, N Lachiche, M Kull, P Flach
Conference: ICTAI’15 – IEEE 27th International Conference on Tools with Artificial Intelligence
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

Rate-oriented point-wise confidence bounds for ROC curves
LAC Millard, M Kull, PA Flach
Conference: ECML-PKDD’14 – The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases
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

Machine Learning research group | Institute of Computer Science, University of Tartu

Home

People

Teaching

Industrial collaborations

Selected publications