STEM / applied
Machine learning for statistics · Graduate · Statistics
Topics
Applied ML for statisticians
- PyTorch or TensorFlow for statisticians (intro)
- GPU training and batching basics
- MLOps and reproducible experiment tracking
- Case studies in vision, NLP, and tabular data
- Transfer learning and fine-tuning (overview)
- Deploying models with monitoring and drift detection
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.