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NGSchool2022 materials

Welcome to the NGSchool2022: Machine Learning in Computational Biology materials. Here you will be able to find links to slides, materials, repositories with code and for some lectures videos.

Some videos are available publicly, while some are restricted to participants of the school - they will be marked with 🔒 and will require a password to access.

Enjoy!

Workshops are marked with 💻, panel discussion with 💬 and lectures with 📣.

Day 0 [15/09/2022]

💻 Introduction to biostatistics

💬 Introduction to Machine Learning

Discussion panel moderated by Gladys Wojciechowska with Nuno Sepúlveda, Kaspar Märtens, German Demidov, Anamaria Elek, Karolina Sienkiewicz and Kasia Kędzierska as panelists.

Day 1 [16/09/2022]

📣 Introduction to linear modeling

💻 Regression models

💻 Regularization for linear models

📣 Unsupervised learning - part 1

Lecture led by Kaspar Märtens and Kasia Kędzierska. Slides will be soon available here.

Day 2 [17/09/2022]

📣 Model explainability

Lecture by Krzysztof Geras. Slides are available here.

💻 Cross-validation and model evaluation

Session led by German Demidov. Slides are available here.

💻 Unsupervised learning - part 2

Session led by Kasia Kędzierska and Kaspar Märtens. Github repository: ngs22_unsupervised.

📣 Clustering in Computational Biology

Lecture by Karolina Sienkiewicz (feat. Kaspar Märtens). Slides are available here.

Day 3 [18/09/2022]

💻 Linear models in gene expression analysis

Session led by Urszula Smyczyńska.

📣 Deep Learning in Computational Biology

Lecture by Leopold Parts. Slides are available here.

📣 Evaluation of AI models

Lecture by Krzysztof Geras. Slides are available here.

Day 4 [19/09/2022]

📣 Natural Language Processing

Lecture by Noura Al Moubayed.

💻 Natural Language Processing

Session led by Noura Al Moubayed & Sean Farrell.

📣 Tree-based methods

Lecture by Maja Kuzman. Slides are available here.

💻 Tree-based methods

Session led by Maja Kuzman & Anamaria Elek. Github repository with materials is available here.

Day 5 [20/09/2022]

💻 Feature selection

Session led by Miron Kursa.

💻 Survival analysis

Session led by German Demidov.

Day 6 - 7 [21-22/09/2022]

Deep Learning in practice

Sessions led by Dmytro Fishman.

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