STEM / applied
Machine learning · Graduate · CS / Programming
Topics
Advanced models
- Kernel methods and Gaussian processes (intro)
- Deep learning architectures: CNNs, RNNs, Transformers (survey)
- Generative models: VAEs, GANs, diffusion (survey)
- Reinforcement learning basics: MDPs, Q-learning (intro)
- Causal inference connections to ML (intro)
Research skills
- Reproducing papers and ablation studies
- Experiment tracking and hyperparameter sweeps
- Responsible ML: fairness, privacy, robustness
- Scaling training: distributed data parallel (intro)
- Reading and presenting NeurIPS/ICML papers
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.