Day 10 01/04/2024 Applications of AI/ML in Multi-omics, Genomics

Recorded Sessions

Day 1 18/03/2024 Overview of Artificial Intelligence (AI) and Machine Learning (ML) Introduction to Biology & Bioinformatics applications of AI/ML (71:23)
Day 2 19/03/2024 Importance and significance of AI/ML in biological research Types of ML algorithms and their relevance in Biology & Bioinformatics (71:02)
Day 3 20/03/2024 Overview of supervised learning algorithms Applications of supervised learning in Biology & Bioinformatics (e.g., sequence classification, protein structure prediction) (64:03)
Day 4 21/03/2024 Clustering algorithms for biological data analysis (e.g., gene expression clustering) (74:48)
Day 5 22/03/2024 Dimensionality reduction techniques (e.g., Principal Component Analysis) and their applications Unsupervised learning applications in Biology & Bioinformatics (77:19)
Day 6 - 26/03/2024 Introduction to Deep Learning and Neural Networks (61:48)
Day 7 27/03/2024 Convolutional Neural Networks (CNNs) for biological analysis Recurrent Neural Networks (RNNs) for sequence data analysis (60:03)
Day 8 28/03/2024 Explore Python coding (60:19)
Day 9 29/03/2024 Data cleaning techniques for biological data Handling missing data in bioinformatics datasets Data normalization and transformation methods Feature selection and dimensionality reduction techniques in bioinformatics (65:39)
Day 10 01/04/2024 Applications of AI/ML in Multi-omics, Genomics (72:48)
Day 11 02/04/2024 Applications of AI/ML in Proteomics and Drug Discovery Protein structure prediction techniques: AI-based modeling, AlphaFold etc (63:53)
Day 12 03/04/2024 Practical Sessions using GitHub repository Hands-on exercises using relevant tools and datasets. (47:33)
Day 13 04/04/2024 Practical Sessions using GitHub repository Hands-on exercises using relevant tools and datasets. (59:26)
Day 14 05/04/2024 Practical Sessions using GitHub repository Hands-on exercises using relevant tools and datasets. (58:12)
Day 15 09/04/2024 Practical Sessions using GitHub repository Hands-on exercises using relevant tools and datasets. (30:36)
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