Machine Learning - behind the scenes

This will be a tutorial series which illustrates underlying concepts of popular Machine Learning algorithms and also draws connections between them in (e.g. bayesian and geometric interpretation).

  1. Linear Regression
  2. Probabilistic Inference
  3. Linear Classification
  4. Constrained Optimization
  5. Support Vector Machine
  6. K-Means vs Gaussian Mixture Model
  7. PCA/PPCA/SVD/Matrix Factorization/Auto Encoders
  8. Variational Inference
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