Robotics: Estimation and Learning by Coursera
Skills you will learn!
Curriculum
15 Topics
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
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Learning Style Preference Questionnaire
Color Learning and Target Detection
6 Topics
WEEK 2 Introduction
Kalman Filter Motivation
System and Measurement Models
Maximum-A-Posterior Estimation
Extended Kalman Filter and Unscented Kalman Filter
Kalman Filter Tracking
7 Topics
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
2D Occupancy Grid Mapping
8 Topics
WEEK 4 Introduction
Odometry Modeling
Map Registration
Particle Filter
Iterative Closest Point
Closing
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Particle Filter Based Localization
Robotics: Estimation and Learning
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