📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of From Petri-Dish to Predictions – AI Meets Microbiology, from foundations to a certified capstone project.
Imaging
• Colony counting and morphology quantification from plate images
• Segmentation of touching and overlapping colonies as the hard case
• Illumination, plate reflection and the imaging setup that decides accuracy
Identification
• MALDI-TOF spectra and library-based identification with its coverage gaps
• Machine learning on microscopy images for morphology-based classification
• Sequence-based identification and where phenotype and genotype disagree
Resistance
• Genotypic AST prediction against phenotypic testing, and current accuracy
• Species-dependent performance and the resistance mechanisms models miss
• Clinical breakpoints, EUCAST and CLSI, and why prediction must be conservative
Communities
• Amplicon and shotgun data reduced to features for machine learning
• Compositionality — relative abundance data breaks standard statistics
• Classifier performance on microbiome data and the overfitting that is endemic
Practice
• Validation against the reference method the laboratory already trusts
• Batch effects between runs, operators and media lots
• Regulatory position for diagnostic use and the limits of research-use claims
Earn government-registered certification in From Petri-Dish to Predictions – AI Meets Microbiology
e-Certificate and e-Marksheet issued on successful completion.
