About this Course
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Flexible deadlines

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Approx. 12 hours to complete

Suggested: 4 weeks of study, 3-4 hours/week...

English

Subtitles: English, Spanish, Chinese (Simplified)
User
Learners taking this Course are
  • Machine Learning Engineers
  • Research Assistants
  • Data Scientists
  • Researchers
  • Engineers

Skills you will gain

Particle FilterEstimationMapping
User
Learners taking this Course are
  • Machine Learning Engineers
  • Research Assistants
  • Data Scientists
  • Researchers
  • Engineers

Course 5 of 6 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 12 hours to complete

Suggested: 4 weeks of study, 3-4 hours/week...

English

Subtitles: English, Spanish, Chinese (Simplified)

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Gaussian Model Learning

9 videos (Total 52 min), 3 readings, 1 quiz
9 videos
WEEK 1 Introduction1m
1.2.1. 1D Gaussian Distribution8m
1.2.2. Maximum Likelihood Estimate (MLE)6m
1.3.1. Multivariate Gaussian Distribution7m
1.3.2. MLE of Multivariate Gaussian4m
1.4.1. Gaussian Mixture Model (GMM)4m
1.4.2. GMM Parameter Estimation via EM7m
1.4.3. Expectation-Maximization (EM)6m
3 readings
MATLAB Tutorial - Getting Started with MATLAB10m
Setting Up your MATLAB Environment10m
Basic Probability10m
Week
2
3 hours to complete

Bayesian Estimation - Target Tracking

5 videos (Total 21 min), 1 quiz
5 videos
Kalman Filter Motivation4m
System and Measurement Models5m
Maximum-A-Posterior Estimation4m
Extended Kalman Filter and Unscented Kalman Filter4m
Week
3
4 hours to complete

Mapping

6 videos (Total 36 min), 1 quiz
6 videos
Introduction to Mapping7m
3.2.1. Occupancy Grid Map6m
3.2.2. Log-odd Update6m
3.2.3. Handling Range Sensor6m
Introduction to 3D Mapping8m
Week
4
3 hours to complete

Bayesian Estimation - Localization

6 videos (Total 23 min), 1 quiz
6 videos
Odometry Modeling5m
Map Registration5m
Particle Filter4m
Iterative Closest Point5m
Closing45s
4.2
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Top reviews from Robotics: Estimation and Learning

By VGFeb 16th 2017

The material is clearly presented. The Matlab exercises complement and reinforce the subject, the level of difficulty is well balanced, thanks for this great course.

By NNJun 20th 2016

This is course is really helpful for beginners to understand how probability is useful in Robotics.Assignments are bit tough but worth the time .

Instructor

Avatar

Daniel Lee

Professor of Electrical and Systems Engineering
School of Engineering and Applied Science

About University of Pennsylvania

The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies. ...

About the Robotics Specialization

The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects....
Robotics

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.