STEM / applied
Machine learning · Graduate · Data science
Topics
Implementation depth
- Implementing algorithms from pseudocode
- Modern libraries (PyTorch/sklearn/jax survey per course)
- Distributed training awareness
- Hyperparameter search at scale
- Benchmarking against strong baselines
- Writing clear experimental sections
Responsible ML
- Dataset documentation and leakage audits
- Fairness interventions survey
- Security: adversarial examples awareness
- Environmental cost of large models (survey)
- Deployment constraints in regulated settings
- Research paper reading and critique
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.