Why is learning from data even possible? To discuss this problem, we need a framework for learning. …

Graph A graph $\mathcal G$ has nodes $\mathcal V$ and edges $\mathcal E$, $$ \mathcal G = ( \mathcal …

Discriminative model: The conditional probability of class label on data (posterior) $p(C_k\mid x)$ …

Contrastive models learn to compare1. Contrastive use special objective functions such as [[NCE]] …

The task of GAN is to generate features $X$ from some noise $\xi$ and class labels $Y$, $$\xi, Y \to …

Gibbs sampling is a sampling method for Bayesian inference and MCMC

ROC is used to judging the performance of classifiers

Tree-based learning

Confusion Matrix It is much easier to understand the confusion matrix if we use a binary …

hypothesis testing is about the probability of alternative hypothesis if the null hypothesis is true, or even more general

In statistics, we work with samples. For example, the sample mean is easily calculated. However, it …

An overview of statistics

Frequent patterns using association rules

Simple artificial neural networks using multilayer perceptron

Essential knowledge of computations

linear methods

A brief overview of machine learning

Unsupervised Learning! Principle components analysis Clustering K-means Clustering Algorithm: …

Algorithms is pretty

C++ as a programming language