All exercises for the course Elements of AI - Building AI

Elements of AI: Building AI - Advanced is an online course by Reaktor and University of Helsinki worth 2 ECTS.

Kalman filter finds the most optimum averaging factor for each consequent state. Also somehow remembers a little bit about the past states.

Solving hangman using probability

Uses some user input, data from the World Values Survey <www.worldvaluessurvey.org>, and Bayes Rule to guess a number of beliefs the user might have. STATUS: In progress.

A simple 1-dimensional Gaussian Naïve Bayes Classifier.

Kidnapped Vehicle (project 6 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

This course covers topics such as overview of sets, counting principles, probability, conditional probability and Bayes’ Rule, discrete Random Variables, continuous Random Variables, Bayesian inference, central limit theorem, regression and Markov Chains

Using 2d histogram filter for localization

This repository is made as supplementary material for a tutorial. The tutorial shows how to use Recurrent Neural Nets as generative models.

The Matlab source code for basic probability used in information theory.

Recursos sobre manejo de la incertidumbre y probabilidad por un agente inteligente, módulo de Modelos de Inteligencia Artificial

A website to simulate book suggestion using Bayes rule

Classification of image pixel into 2 classes using Baye's Decision Rule

Project for the course ENPM667 @ UMD. Code base and install instructions for a Point Cloud Regression and Camera Pose Estimation network using a Kalman Filter based loss function.

Deliverables relating to the Foundations of Data Science University Unit