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
<p>..</p>
> **Example to use LaTEX**
$$\begin{aligned} &\text { Table 1: A table without vertical lines. }\ &\begin{array}{lcc} \hline & \text { Treatment A } & \text { Treatment B } \ \hline \text { John Smith } & 1 & 2 \ \text { Jane Doe } & - & 3 \ \text { Mary Johnson } & 4 & 5 \ \hline \end{array} \end{aligned}$$