📚 Syllabus & Course Curriculum
AI & Machine Learning in Healthcare
Module-by-module breakdown of Next-Generation Bioinformatics Using Machine Learning and Deep Learning, from foundations to a certified capstone project.
Setup
• Encoding sequence, structure and expression for learning algorithms
• High-dimension low-sample-size regimes and regularisation strategy
• Splitting by homology or patient to avoid inflated performance
Classical
• Regularised regression and tree ensembles on omics features
• Feature selection and the instability of selected gene panels
• Nested cross-validation for honest performance estimates
Deep Learning
• CNNs for genomic sequence and regulatory prediction
• Graph neural networks over molecular and interaction graphs
• Transformers and pretrained biological foundation models
Interpretation
• Attribution methods and in-silico mutagenesis
• Generating testable hypotheses rather than post-hoc narratives
• Distinguishing a learned biological signal from a dataset artefact
Benchmarking
• Community benchmarks and the pitfalls of leaderboard chasing
• Baselines that must be reported for a claim to be credible
• Compute, reproducibility and releasing code that runs
Earn government-registered certification in Next-Generation Bioinformatics Using Machine Learning and Deep Learning
e-Certificate and e-Marksheet issued on successful completion.
