.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell

Review of Statistics with Resampling Techniques and Linear Algebra

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Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods

Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway

Developing a code for doing neural networks with back propagation

Making your own Bootstrap: Changing the Level of the Decision Tree

Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent

Diagonalize the sample covariance matrix to obtain the principal components

Geometric Interpretation and link with Singular Value Decomposition

Deriving the back propagation code for a multilayer perceptron model

Stochastic variables and the main concepts, central moments, the variance

Probability Distribution Functions, other important distribution

Definition of Correlation Functions and Standard Deviation, sample variance

Correlation function and which random number generators should I use

Code to demonstrate the calculation of the autocorrelation function

Examples of likelihood functions used in logistic regression and nueral networks

Optimization, the central part of any Machine Learning algortithm