• ## Notes on Machine Learning 13: Graphical Models

(ML 13.1) (ML 13.2) Directed graphical models - introductory examples (Directed) Graphica “Models” aka “Bayesian” networks Better name would be “conditional independence diagrams” of probability distributions Key notions: factorization of probability distributions notational device useful for visualization of (a) conditional independence properties (b) inference algorithms (DP, MCMC) Why conditional independence...

• ## Notes on Machine Learning 12: Model selection

(ML 12.1) Model selection - introduction and examples “Model” selection really means “complexity” selection! Here, complexity $\approx$ flexibility to fit/explain data Example (Linaer regression with MLE for $w$) $f(x) = w^T\varphi(x)$ Given data $x \in \mathbb{R}$, consider polynomial basis $\varphi(x) = x^k$, $\varphi = (\varphi_0, \varphi_1, \ldots, \varphi_B)$ Turns out...

• ## Notes on Machine Learning 10: Bayesian linear regression

(ML 10) Baeysian linear regression Linear regression is one of the most powerful general purpose tools. Using MLE for ligression often leads to overfitting, which can be a sever problem. Using MAP can solve the problem of overfitting, but we don’t have rpresentation of uncertainty in this case (not only...