Algorithm is a library of tools that is used to create intelligent applications.

Outlier detection for categorical data

Feedback particle filters for nonlinear stochastic filtering and data assimilation problems

📦 📊 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

Analysing the dataset from California DMV to find out leading causes of disengagements in AVs.

This is a seminar paper that I wrote on the topic of conditional expectation as part of my measure-theoretic probability course.

Curso de adquisición y análisis de datos con técnicas multivariadas, teoría de gráficas, y redes.

Assigments done as part of coursework

To build a probabilistic model from in python the training set (1997 emails of Ham/Spam ), we parse the all files and build a vocabulary with all the words it contains. Then, for each word, compute their frequencies and conditional probabilities for each class.Then from testing set (800 emails) we compute the score of both class as given by our classifier and based on that score we classify whether it is spam or not. We also calculate accuracy, precision, recall, F1-measure and confusion matrix for each class (spam and ham).

Probability and Statistics - R Manual

A very primitive type of recommender system

Conditional probability for the RTBM.

A simple example on how to analyze a text in python and generate your own text using bigrams

Monty Hall problem

Various algorithms and data structures for educational purposes

Estimate conditional probabilities, compare data distributions, and perform data transformations to analyze employee absences