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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/
Last synced: about 2 months ago
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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 (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-01-28T20:07:18.000Z (over 1 year ago)
- Last Synced: 2024-07-21T19:49:25.125Z (2 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
Awesome Lists containing this project
- awesome - Aalto Course - Advanced Probablistic Methods
README
# 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**
$$\begin{aligned}
&\text { Table 1: A table without vertical lines. }\\
&\begin{array}{lcc}
\hline & \text { Treatment A } & \text { Treatment B } \\
\hline \text { John Smith } & 1 & 2 \\
\text { Jane Doe } & - & 3 \\
\text { Mary Johnson } & 4 & 5 \\
\hline
\end{array}
\end{aligned}$$