Syllabus
Chapter 0 · Introduction
🔥A friendly tour of explicit vs implicit models, a whirlwind of applications, and why safety matters. Includes quizzes, animations, and a tiny poem.
Chapter 1 · Divergence Measures
📏KL, JS, Wasserstein, TV, and $f$-divergences with interactive quizzes, true/false, and mini-labs.
Chapter 2 · Deep Learning
🧠Perceptrons to Transformers—history, feedforward, backprop, batch norm, RNNs/LSTMs, masked attention, quizzes, and poems.
Chapter 3 · Probabilistic Graphical Models
🧩Factorization, conditional independence, hidden/observed setups, inference questions, quizzes, and memory poems.
Chapter 4 · Gaussian Mixture Models
🥣Mixture basics, MLE + EM updates, variational inference, code demo, quizzes, and poems.
Chapter 5 · GANs
🎭Generator vs discriminator, JS view, EM-style training loop, optimal D, stabilization tips, quizzes, and poems.
Chapter 6 · Wasserstein GANs
🌊KR duality, 1-Lipschitz critics, WGAN-GP, full proofs, spot-the-mistake checks, and interactive quizzes.
Chapter 7 · Computer Vision Problems
👁️Low & high-level vision, texture synthesis, denoising, quilting, NPR, and classical ideas that shaped modern generative models.
Chapter 8 · GAN Architectures for Vision
🏗️Conditional GANs, Progressive training, Pix2Pix, StyleGAN, GigaGAN, GauGAN—specialized architectures for vision tasks.
Chapter 9 · Optimization Solvers
⚙️Gradient descent, GDA, Nash equilibrium, game theory, and advanced solvers for min-max optimization.
Chapter 10 · Sinkhorn Generative Modeling
🚚Optimal transport, entropy regularization, Sinkhorn algorithm, matrix scaling, and fast generative training.
Chapter 11 · Variational Autoencoders
🎲Autoencoders, PCA connection, probabilistic latent spaces, reparameterization trick, ELBO, and generative modeling.
Chapter 12 · VAE Mathematical Theory
🎓Divergence minimization, variational inference, complete proofs, KL for Gaussians, reparameterization trick, and posterior collapse.
Chapter 13 · Diffusion Models
🌊Hierarchical VAE, VDM, DDPM algorithms, score matching, Langevin dynamics, and denoising score matching with complete derivations.
Chapter 14 · Optimization Methods
📈Gradients, Jacobians, level curves, gradient descent/ascent algorithms, Cauchy's original 1847 paper, and convergence guarantees with complete proofs.
Explicit Density Models
🧮Likelihoods, VAEs, and flows that keep math honest.
Coming soon
Implicit Density Models
🤝GANs as creative rivals plus diffusion thinking and autoregressive tricks.
Coming soon
Evaluation & Safety
🛡️Metrics, human evals, and building responsibly.
Coming soon
How this course feels
- Short bursts of theory, followed by playful experiments.
- Animations that show sampling, density shaping, and mode collapse.
- Micro-quizzes with instant feedback (and pretend internet points).
- Field notes from medicine, sports, entertainment, and education.
- Safety interludes so we keep the creativity kind.
Mini mantra
"Sample boldly, evaluate kindly, repeat with a grin."
Yes, there will be the occasional rhyme. Generators like rhythm.
Ready to jam with probabilities?
Start with the Introduction chapter and unlock the first set of interactive labs.
Go to Introduction →