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‎README.md‎

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| Day | Topics |
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| --- | -------------------------------------------------------------------------------------------------------------- |
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| 1 | [ML terminology and ML in Bioinformatics](Day1/0_Introduction_ml_terminology.pdf) |
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| | [Unsupervised Learning: Clustering (K-Means Clustering, Hierarchical clustering, Clustering evaluation metrics)](Day1/1_Unsupervised_Learning.pdf) |
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| | [Unsupervised Learning: Dimensionality reduction (Principal component analysis - PCA)](Day1/1_Unsupervised_Learning.pdf) |
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| 1 | [ML terminology and ML in Bioinformatics](content/Day1/0_Introduction_ml_terminology.pdf) |
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| | [Unsupervised Learning: Clustering (K-Means Clustering, Hierarchical clustering, Clustering evaluation metrics)](content/Day1/1_Unsupervised_Learning.pdf) |
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| | [Unsupervised Learning: Dimensionality reduction (Principal component analysis - PCA)](content/Day1/1_Unsupervised_Learning.pdf) |
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| | [Hands-on session demonstrating PCA and clustering in cancer genomics](https://naicno.github.io/BioNT_Module2_handson/1.PCA_n_Clustering/) |
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| 2 | [Classification: Logistic regression; Tree-based methods; Matrices for classification evaluation](Day2/2_ML_classification_topics.pdf) |
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| 2 | [Classification: Logistic regression; Tree-based methods; Matrices for classification evaluation](content/Day2/2_ML_classification_topics.pdf) |
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| | [Hands-on session demonstrating Logistic regression in cancer genomics](https://naicno.github.io/BioNT_Module2_handson/2.Logistic_regression/) |
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| | [Regression: Regression mechanics, Loss function, Regularised regression, Matrices for regression evaluation](Day2/3_ML_Regression_topics.pdf) |
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| 3 | [Model validation and optimisation (Overfitting and underfitting, Standardising Data, Handling missing data)](Day3/4_Model_optimization_and_validation.pdf) |
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| | [Regression: Regression mechanics, Loss function, Regularised regression, Matrices for regression evaluation](content/Day2/3_ML_Regression_topics.pdf) |
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| 3 | [Model validation and optimisation (Overfitting and underfitting, Standardising Data, Handling missing data)](content/Day3/4_Model_optimization_and_validation.pdf) |
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| | [Model validation and optimisation (K-fold cross-validation)](Day3/4_Model_optimization_and_validation.pdf) |
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| | [Hands-on session: ML workflow with biological data](https://naicno.github.io/BioNT_Module2_handson/3.ML_workflow/) |
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| 4 | [Introduction to deep learning (Basic concepts of Neural Networks - NN; Simple NN with PyTorch)](Day4/introduction_to_deep_learning.pdf) |
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| 4 | [Introduction to deep learning (Basic concepts of Neural Networks - NN; Simple NN with PyTorch)](content/Day4/introduction_to_deep_learning.pdf) |
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| | [Hands-on session demonstrating deep-learning-based variant calling via DeepVariant](https://naicno.github.io/BioNT_Module2_handson/4.DeepVariant/) |
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| 5 | [Notes for the video course: Introduction - NGS)](content/Day5/1.NGS_introductions.pdf); [Notes for the video course: Introduction - NGS)](content/Day5/2.ACC_NGS.pdf) |
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| 5 | [Notes for the video course: Introduction - NGS)](content/Day5/1.NGS_introductions.pdf); [Notes for the video course: Accelerated NGS)](content/Day5/2.ACC_NGS.pdf) |
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| | Bonus materials: [Introduction to Accelerated Genomics (NGS data analysis, GPU introduction)](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics) |
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| | Bonus materials: [GPU introduction](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics) |
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| | Bonus materials: [Docker introduction](https://training.pages.sigma2.no/tutorials/gpu-intro/) |

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