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

Requirements

Good grounding in basic machine learning. Programming skills in any language (ideally Python/R).

 21 Hours

Testimonials (4)

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