AutoML for Biomedical Prediction Tasks — Hands-on

Build high-quality predictive models faster with AutoML. Learn to define search spaces, orchestrate robust hyperparameter optimization, automate feature engineering, and generate transparent model reports for regulatory-grade reproducibility. Covers tabular omics, clinical records, imaging-derived features, and time-aware data.

AutoML for Biomedical Prediction Tasks

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Session Index

Session 1 — AutoML Foundations & Search Spaces Session 2 — Hyperparameter Optimization & Pipelines Session 3 — AutoML for Bio Data & Ensembling Session 4 — Mini Capstone: AutoML Benchmark & Report

Session 1

Fee: Rs 12,600

AutoML Foundations & Search Spaces

What to automate in ML lifecycles

preprocessing model choice HPO & ensembling

Search space design & constraints

numeric/categorical conditional params resource budgets

Tooling landscape

auto-sklearn TPOT FLAML

Session 2

Fee: Rs 17,600

Hyperparameter Optimization & Pipelines

HPO strategies & schedulers

Bayesian/TPE Hyperband/ASHA CMA-ES/BOHB

Pipelines & data leakage control

sklearn pipelines stratified CV/nested CV time-aware splits

Tuning at scale

Optuna Ray Tune early stopping

Session 3

Fee: Rs 22,800

AutoML for Bio Data & Ensembling

Domain specifics: omics & clinical

high-p, low-n class imbalance feature stability

Model selection & ensembling

stacking/blending calibration cost-sensitive metrics

Governance & reporting

model cards datasheets reproducibility logs

Session 4

Fee: Rs 28,400

Mini Capstone: AutoML Benchmark & Report

Benchmark multiple AutoML toolchains on a real dataset

Theory + Practical

Deliver robust, reproducible artifacts

notebook/script metrics dashboard model card & report

Optional: lightweight deployment demo

CLI or Flask app env files re-run scripts

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AutoML for Biomedical Prediction Tasks Training | HPO, Ensembling, Reproducible Benchmarks

AutoML for Biomedical Prediction Tasks — Hands-on