https://fartaha.github.io/ml-advanced-probabilistic-methods/

Summary of CS-E4820 - Machine Learning: Advanced Probabilistic Methods
https://fartaha.github.io/ml-advanced-probabilistic-methods/

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Summary of CS-E4820 - Machine Learning: Advanced Probabilistic Methods

• Host: GitHub
• URL: https://fartaha.github.io/ml-advanced-probabilistic-methods/
• Owner: fartaha
• License: bsd-3-clause
• Created: 2023-01-13T17:33:42.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-28T20:07:18.000Z (almost 2 years ago)
• Last Synced: 2024-08-01T01:28:52.990Z (3 months ago)
• Language: HTML
• Homepage: https://fartaha.github.io/ml-advanced-probabilistic-methods/
• Size: 7.36 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome - Aalto Course - Advanced Probablistic Methods

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# ml-advanced-probabilistic-methods

Summary of CS-E4820 - Machine Learning: Advanced Probabilistic Methods @ Aalto University

> Summary of Lec 1

Summary of Lec 1

     

  Main Book

  



     

   ### Ingredients of probabilistic modeling

    1. Models

      * Bayesian networks

      * Sparse Bayesian linear regression

      * Gaussian mixture models

      * latent linear models

    2. Methods for inference

      * maximum likelihood

      * maximum a posteriori (MAP)

      * Laplace approximation

      * expectation maximization (EM)

      * Variational Bayes (VB)

      * Stochastic variational inference (SVI)

      * ::MCMC methods (missing)::

    3. Ways to select between models

     

     
..

     > **Example to use LaTEX**

     

$$\begin{aligned}

&\text { Table 1: A table without vertical lines. }\\

&\begin{array}{lcc}

\hline & \text { Treatment A } & \text { Treatment B } \\

\hline \text { John Smith } & 1 & 2 \\

\text { Jane Doe } & - & 3 \\

\text { Mary Johnson } & 4 & 5 \\

\hline

\end{array}

\end{aligned}$$