Merge pull request #4 from NAICNO/Day5-video-course

Day5 video course

Author: PubuduSaneth

.DS_Store

@@ -4,7 +4,7 @@
 
 ![alt text](content/images/image.png)
 
-[BioNT](https://biont-training.eu/training.html) is an international consortium dedicated to providing high-quality training and fostering a community for digital skills in the biotechnology and biomedical sectors. The project's training model is built on three core missions:
+[BioNT](https://biont-training.eu/training.html) is an international consortium dedicated to providing high-quality training and fostering a community for digital skills in the biotechnology and biomedical sectors.
 
 ## Course name
 
@@ -41,7 +41,7 @@ Module 1 (optional) provides a solid foundation in scientific computing with Pyt
 
 Module 2 spans five full days and begins by introducing core concepts in machine learning. On the first day, the participants will be introduced to unsupervised learning, and they will implement clustering algorithms and dimensionality reduction techniques using real-world genomics data. The workshop then dives into supervised learning with a focus on classification and regression, including logistic regression and tree-based methods. Participants will construct and evaluate ML models, perform cross-validation, and tune hyperparameters in hands-on sessions tailored to cancer genomics datasets. Later sessions introduce deep learning concepts and the PyTorch framework. Participants will learn to build and train simple neural networks and explore a deep learning-based bioinformatics tool used in genomic variant calling. The final day introduces accelerated genomics through GPU-powered workflows. Participants will learn about GPU technology and how to use containerized bioinformatics tools. They will also implement high-performance, GPU-accelerated pipelines using Parabricks.
 
-### Learning Outcomes: 
+### Learning Outcomes:
 
 By the end of this workshop, you will be able to:
 
@@ -59,22 +59,35 @@ By the end of this workshop, you will be able to:
 
 | Day | Topics                                                                                                         |
 | --- | -------------------------------------------------------------------------------------------------------------- |
-|  1   | [ML terminology and ML in Bioinformatics](Day1/0_Introduction_ml_terminology.pdf)                                                                                                                     |
-|     | [Unsupervised Learning: Clustering (K-Means Clustering, Hierarchical clustering, Clustering evaluation metrics)](Day1/1_Unsupervised_Learning.pdf)                                                                                                                     |
-|     | [Unsupervised Learning: Dimensionality reduction (Principal component analysis - PCA)](Day1/1_Unsupervised_Learning.pdf)                                                                                                                     |
+|  1   | [ML terminology and ML in Bioinformatics](content/Day1/0_Introduction_ml_terminology.pdf)                                                                                                                     |
+|     | [Unsupervised Learning: Clustering (K-Means Clustering, Hierarchical clustering, Clustering evaluation metrics)](content/Day1/1_Unsupervised_Learning.pdf)                                                                                                                     |
+|     | [Unsupervised Learning: Dimensionality reduction (Principal component analysis - PCA)](content/Day1/1_Unsupervised_Learning.pdf)                                                                                                                     |
 |     | [Hands-on session demonstrating PCA and clustering in cancer genomics](https://naicno.github.io/BioNT_Module2_handson/1.PCA_n_Clustering/)                                                            |
-| 2   | [Classification: Logistic regression; Tree-based methods; Matrices for classification evaluation](Day2/2_ML_classification_topics.pdf)                                                                                                                     |
+| 2   | [Classification: Logistic regression; Tree-based methods; Matrices for classification evaluation](content/Day2/2_ML_classification_topics.pdf)                                                                                                                     |
 |     | [Hands-on session demonstrating Logistic regression in cancer genomics](https://naicno.github.io/BioNT_Module2_handson/2.Logistic_regression/)                                                                 |
-|     | [Regression: Regression mechanics, Loss function, Regularised regression, Matrices for regression evaluation](Day2/3_ML_Regression_topics.pdf)                                                                                                                     |
-| 3   | [Model validation and optimisation (Overfitting and underfitting, Standardising Data, Handling missing data)](Day3/4_Model_optimization_and_validation.pdf)                                                                                                                     |
+|     | [Regression: Regression mechanics, Loss function, Regularised regression, Matrices for regression evaluation](content/Day2/3_ML_Regression_topics.pdf)                                                                                                                     |
+| 3   | [Model validation and optimisation (Overfitting and underfitting, Standardising Data, Handling missing data)](content/Day3/4_Model_optimization_and_validation.pdf)                                                                                                                     |
 |     | [Model validation and optimisation (K-fold cross-validation)](Day3/4_Model_optimization_and_validation.pdf)                                                                                                                     |
 |     | [Hands-on session: ML workflow with biological data](https://naicno.github.io/BioNT_Module2_handson/3.ML_workflow/)                                                                                                 |
-| 4   | [Introduction to deep learning (Basic concepts of Neural Networks - NN; Simple NN with PyTorch)](Day4/introduction_to_deep_learning.pdf)                                                                                                                     |
+| 4   | [Introduction to deep learning (Basic concepts of Neural Networks - NN; Simple NN with PyTorch)](content/Day4/introduction_to_deep_learning.pdf)                                                                                                                     |
 |     | [Hands-on session demonstrating deep-learning-based variant calling via DeepVariant](https://naicno.github.io/BioNT_Module2_handson/4.DeepVariant/)                                                                                  |
-| 5   | [Introduction to Accelerated Genomics (NGS data analysis, GPU introduction)](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                 |
-|     | [GPU introduction](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                                                                           |
-|     | [Docker introduction](https://training.pages.sigma2.no/tutorials/gpu-intro/)                                                                                        |
+| 5   | Notes for the video course: [Introduction to Accelerated Genomics](content/Day5/Introduction_to_Accelerated_Genomics.pdf) |
+|     | Bonus materials: [Introduction to Accelerated Genomics (NGS data analysis, GPU introduction)](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                 |
+|     | Bonus materials: [GPU introduction](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                                                                           |
+|     | Bonus materials: [Docker introduction](https://training.pages.sigma2.no/tutorials/gpu-intro/)                                                                                        |
 |     | [Hands-on session on implementing accelerated Genomics workflows with Parabricks on VM with GPUs](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics/04.Hands-on/)                                                 |
 
 
 - [Link to the Sphinx Page](https://naicno.github.io/BioNT_AppliedML_Learning_Materials/)
+
+## Contact
+
+- [Sabry Razick](https://www.usit.uio.no/om/organisasjon/ffu/bt/ansatte/sabryr/index.html)
+- [Pubudu Samarakoon](https://www.usit.uio.no/om/organisasjon/ffu/bt/ansatte/pubuduss/index.html)
+
+## Coordinator
+
+- European Molecular Biology Laboratory
+- Contact:
+  - EMBL, Meyerhofstr. 1, 69126 Heidelberg, Germany
+  - contact@biont-training.eu

README.md

@@ -52,7 +52,8 @@ Module 2 spans five full days and begins by introducing core concepts in machine
 |     | [Hands-on session: ML workflow with biological data](https://naicno.github.io/BioNT_Module2_handson/3.ML_workflow/)                                                                                                 |
 | 4   | [Introduction to deep learning (Basic concepts of Neural Networks - NN; Simple NN with PyTorch)](Day4/introduction_to_deep_learning.pdf)                                                                                                                     |
 |     | [Hands-on session demonstrating deep-learning-based variant calling via DeepVariant](https://naicno.github.io/BioNT_Module2_handson/4.DeepVariant/)                                                                                  |
-| 5   | [Introduction to Accelerated Genomics (NGS data analysis, GPU introduction)](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                 |
+| 5   | Notes for the video course: [Introduction to Accelerated Genomics](content/Day5/Introduction_to_Accelerated_Genomics.pdf) |
+|     | [Introduction to Accelerated Genomics (NGS data analysis, GPU introduction)](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                 |
 |     | [GPU introduction](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics)                                                                                           |
 |     | [Docker introduction](https://training.pages.sigma2.no/tutorials/gpu-intro/)                                                                                        |
 |     | [Hands-on session on implementing accelerated Genomics workflows with Parabricks on VM with GPUs](https://coderefinery.github.io/BioNT_Lesson_Accelerated_Genomics/04.Hands-on/)                                                 |