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Solutions for Pattern Recognition and Machine Learning - Christopher M. Bishop
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1. Introduction
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LICENSE.md
README.md
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README.md

Solutions for Pattern Recognition and Machine Learning - Christopher M. Bishop

This repo contains (or at least will eventually contain) solutions to all the exercises in Pattern Recognition and Machine Learning - Christopher M. Bishop, along with useful code snippets to illustrate certain concepts.

Note: View the solutions at https://priyathamkat.com/bishop-prml/ as GitHub doesn't render LaTeX in .ipynb notebooks properly.

Contents (progress)

  1. Introduction (15/41)
  2. Probability Distributions (0/61)
  3. Linear Models for Regression (0/24)
  4. Linear Models for Classification (0/26)
  5. Neural Networks (0/41)
  6. Kernel Methods (0/27)
  7. Sparse Kernel Machines (0/19)
  8. Graphical Models (0/29)
  9. Mixture Models and EM (0/27)
  10. Approximate Inference (0/39)
  11. Sampling Methods (0/17)
  12. Continuous Latent Variables (0/29)
  13. Sequential Data (0/34)
  14. Combining Models (0/17)

Please raise an issue if you notice any inaccuracies.

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