Course Outline
- Backprop, modular models
- Logsum module
- RBF Net
- MAP/MLE loss
- Parameter Space Transforms
- Convolutional Module
- Gradient-Based Learning
- Energy for inference
- Objective for learning
- PCA, NLL
- Latent Variable Models
- Probabilistic LVM
- Loss Function
- Handwriting recognition
- Good grounding in basic machine learning.
- Programming skills in any language (ideally Python/R).
21 hours (usually 3 days including breaks)
