Lectures
- Lecture 1: Introduction, Motivation and Applications of Deep Generative Models, Course Logistics
- Lecture 2: Probability basics: Random Variables, CDF/PDF, Multivariate Gaussian, Asymptotics
- Lecture 3: Estimation Basics: Maximum Likelihood, MLE Examples, MAP Estimation, Kullback-Leibler Divergence
- Lecture 4: Shallow Generative Models, Gaussian Mixture Model, Expectation Maximization (EM) Algorithm
- Lecture 5: Autoregressive Models: Introduction, NADE, RNADE
- Lecture 6: Autoregressive Models: WaveNet (slides by Vassilis Tsiaras)
- Lecture 7: Autoregressive Models: Transformer (slides by Lucas Beyer)
- Lecture 8: Normalizing Flows: Definition, Properties, Change of Variables, Planar Flows
Tutorials
- Tutorial 1: Python basics: numpy, pandas, matplotlib, pytorch
- Tutorial 2: PyTorch basics: multivariate Gaussian, data loader, NN intro
- Tutorial 3: Preparatory for 1st Homework
- Tutorial 4: Multilayer Perceptron, GPU Training, 2D CNNs, Feature Visualization, Weight Normalization, Skip & Residual Connections
- Tutorial 5: Recurent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Linear Units (GRUs)