School S09 · F02
Machine Learning
Classical + modern ML for real business problems.
Grounds engineers in the mathematics and engineering of ML — regression, trees, deep learning — and how to ship models that don't rot in production.
Learning outcomes
What graduates can do.
- 01Frame business problems as ML problems
- 02Train, evaluate and deploy models end-to-end
- 03Detect drift, retrain and version models
- 04Communicate uncertainty to non-technical stakeholders
Curriculum
9 courses.
Delivered as a portfolio-based sequence. Every course culminates in shipped work reviewed by working practitioners.
- 01Math for ML (Linear Algebra, Probability, Optimization)
- 02Classical ML (Regression, Trees, Ensembles)
- 03Deep Learning Foundations
- 04Feature Engineering & Data Quality
- 05Experiment Tracking & Reproducibility
- 06MLOps & Model Deployment
- 07Drift Detection & Retraining Loops
- 08Responsible ML & Fairness
- 09Capstone: Ship an ML System to Production
Tools & technologies
PythonPyTorchscikit-learnMLflowWeights & BiasesModal
Career tracks
- ML Engineer
- Applied Scientist
- MLOps Engineer
Featured programs
Flagship programs anchored in this school.
Other schools in this faculty