Completed
– 1. PCA
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Classroom Contents
PCA, AE, K-Means, Gaussian Mixture Model, Sparse Coding, and Intuitive VAE
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- 1 – Welcome to class
- 2 – Training methods revisited
- 3 – Architectural methods
- 4 – 1. PCA
- 5 – Q&A on Definitions: Labels, unconditional, and un, selfsupervised learning
- 6 – 2. Auto-encoder with Bottleneck
- 7 – 3. K-Means
- 8 – 4. Gaussian mixture model
- 9 – Regularized EBM
- 10 – Yann out of context
- 11 – Q&A on Norms and Posterior: when the student is thinking too far ahead
- 12 – 1. Unconditional regularized latent variable EBM: Sparse coding
- 13 – Sparse modeling on MNIST & natural patches
- 14 – 2. Amortized inference
- 15 – ISTA algorithm & RNN Encoder
- 16 – 3. Convolutional sparce coding
- 17 – 4. Video prediction: very briefly
- 18 – 5. VAE: an intuitive interpretation
- 19 – Helpful whiteboard stuff
- 20 – Another interpretation
