UPenn - Robotics: Estimation and Learning 

Course Introduction

WEEK 1 Introduction

1.2.1. 1D Gaussian Distribution

1.2.2. Maximum Likelihood Estimate (MLE)

1.3.1. Multivariate Gaussian Distribution

1.3.2. MLE of Multivariate Gaussian

1.4.1. Gaussian Mixture Model (GMM)

1.4.2. GMM Parameter Estimation via EM

1.4.3. Expectation-Maximization (EM)

MATLAB Tutorial - Getting Started with MATLAB

Setting Up your MATLAB Environment

Basic Probability

WEEK 2 Introduction

Kalman Filter Motivation

System and Measurement Models

Maximum-A-Posterior Estimation

Extended Kalman Filter and Unscented Kalman Filter

WEEK 3 Introduction

Introduction to Mapping

3.2.1. Occupancy Grid Map

3.2.2. Log-odd Update

3.2.3. Handling Range Sensor

Introduction to 3D Mapping

WEEK 4 Introduction

Odometry Modeling

Map Registration

Particle Filter

Iterative Closest Point

Closing