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
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$1,162 · Machine learning · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.