#Kalman Filter #Particle Filter #Graphical Models

Advanced Machine Learning

1 Libraries 2 Graphical Models 2.0.1 D-seperation 2.1 1)Show that multiple runs of the hill-climbing algorithm can return non-equivalent Bayesian network (BN) structures. Explain why this happens. Use the Asia dataset which is included in the bnlearn package. To load the data, run data(“asia”). 2.2 2) Learn a BN from 80 percent of the Asia dataset. The dataset is included in the bnlearn package. To load the data, run data(“asia”). ...

#Machine Learning #Cheatbook

Machine Learning CheatBook

1 Simple Tasks 1.1 Library 1.2 Reading Excel 1.3 Spliting the Datasets 1.3.1 Divide into train/test 1.3.2 Train/test/validation 1.4 Custom code for Cross-Validation 1.5 Misclassification error calculation 1.6 Assume that mortality y is Poisson distributed, where Y!=12..n . Write an R code computing the minus-loglikelihood of Mortality values for a given lambda. Compute the minus log-likelihood values for lambda=10,110,210,…,2910 and produce a plot showing the dependence of the minus log-likelihood on the value of lambda. ...

#Visualization #ggplot2 #Cheatbook

Visualization CheatBook

1 Reading Data 2 Data Mugging 2.1 Quantile Computation 2.2 Scaling the Data 2.3 Distance Matrix between rows 2.4 Non-metric MDS 2.5 Principle Component Analysis 2.6 Types of Projection 2.7 Types of easing 2.8 Sorting dataset 2.9 Colour selection palette 2.9.1 Adding custom colours without palette 3 Single Plots 3.1 Density Plot 3.1.1 Density Plot with Outlier Highlight using GGplot2 3.1.2 Density Plot with Outlier Highlight using Plotly (converting from ggplot2) 3. ...

#Linear Congruential Method #Inverse CDF Method #MCMC

Computational Statistics

1 Libraries 2 Distributions 2.1 Relationship between distributions 2.2 Bernoulli Distribution 2.3 Beta Distribution 2.4 Exponential distribution 2.5 Pareto Distribution 2.6 Uniform Distribution 2.7 Normal Distribution 2.8 Extra suggestions 3 Random Sampling from Uniform distribution 3.1 Sampling based probabilities proportional to the number of inhabitants of the city 4 Numeric Precision 5 Random Number Generation 5.1 Implementing the Varience 6 Scaling to get better results 7 Split the data into train and test 8 Loess Model with brute force of finding in minimizing of a function 9 Optimize function to find the minimum 10 Optim function to find the minimum 10. ...