Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

This tutorial demonstrates how to compute maximum likelihood estimates of the parameters of a Gaussian distribution both analytically and using gradient descent.

StochasticA is a textbook / website for an “Introduction to Stochastic Signal Processing”. Materials for this website can be found here. Be sure to read the README.md document if you want to know more about the implementation.

Various filters developed in Matlab

Udacity Flying Car Nanodegree

Privacy Enabled Crowdsourced Transmitter Location: MS thesis work

This repository is a part of the Estimation theory laboratory exercises at the Faculty of Electrical Engineering and Computing, University of Zagreb

Assignments for class in signal processing and data analysis

A user-friendly toolkit for engineers and researchers to address the problem of aligning measurements from gyroscopes mounted on the same rigid body.

Python code for a particle filter estimator using pyParticleEst library

Programming assignments and final project of stochastic processes course

Exam project of the course "Learning and Estimation of Dynamical Systems M", Unibo.

A list of projects from my Udacity course on Autonomous Flight