Standard syllabus
Machine learning · Graduate · Data science
Topics
Foundations
- Statistical learning framework: risk, ERM, and generalization
- Bias–variance tradeoff and complexity control
- Linear models, kernels, and regularization theory (intro)
- Optimization for ML: GD, SGD, and convexity survey
- Probabilistic models and MLE/MAP intuition
- Representation learning motivation
Core methods
- Trees, boosting, and random forests in depth
- SVMs and kernel methods (course-dependent)
- Neural networks: backprop and architectures survey
- Unsupervised learning: clustering, PCA, embeddings
- Sequence and graph model survey (optional)
- Generative models overview (VAE/GAN/diffusion survey)
Evaluation and theory practice
- Proper scoring rules and calibration
- Cross-validation theory pitfalls
- Multiple testing in model search
- Domain shift and robustness (intro)
- Interpretability methods and limits
- Reproducibility of ML experiments
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.