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Robotics

Estimation and Learning

This course is a part of Robotics, a 7-course Specialization series from Coursera.

How can robots determine their state and properties of the surrounding environment from noisy sensor measurements in time? In this module you will learn how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Specific topics that will be covered include probabilistic generative models, Bayesian filtering for localization and mapping.
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University of Pennsylvania

Rating 3.5 based on 80 ratings
Length 5 weeks
Effort 4 weeks of study, 3-4 hours/week
Starts Jul 6 (7 days ago)
Cost $49
From University of Pennsylvania via Coursera
Instructor Daniel Lee
Download Videos On all desktop and mobile devices
Language English
Subjects Engineering Mathematics
Tags Physical Science And Engineering Mechanical Engineering Math And Logic

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What people are saying

According to other learners, here's what you need to know

kalman filter in 9 reviews

This course covers some very important techniques in modern robotics including Kalman filters, mapping, and Particle filters.

Although the course is structured properly, the lectures are horrible, explanation for kalman filter lasts couple of minutes,while in universities the topic is studied and implemented as thesis over 6 months, week 4 also throws very poor insight on particle filter, week1 and week3 were better explained.

Leanring of mechanism and implementation of Kalman filter and particle filter from experiment is very interesting for me.

But the other two weeks on Kalman filter and Particle Localization were little disappointing.

Good intro to Kalman filters.

4 or 5 minute lectures on important concepts such as particle filter and Kalman Filter is not at all adequate.

This is a really comprehensive course which gave me a good knowledge about Gaussian Model and Kalman Filter ... Good course schedule, but videos in week 2 and week 4 really need some rework.

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particle filter in 6 reviews

Poor explanations in the lectures especially for particle filter.It doesn't go deep into why and how the method was developed in a theoretical way.

Wrong formula is shown for one of the important concepts (particle filter).

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programming assignments in 4 reviews

A tough course with few hours of lecture material and some good programming assignments.You will be satisfied by those assignments however .

Some videos are perfect if you want to sleep...Also there was some mistakes in the programming assignments (more or less fixed now) and maybe a bit more details or example code would help.

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very interesting in 4 reviews

Great course ,very interesting ,for student with little background like me ,I think more supplemetary materials is needed to get full understanding of such a tough course.thank you,Professor Lee and all the teaching staff.

The material covered is very interesting.

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especially the last in 3 reviews

The assignment is not designed very well especially the last one.

Difficult course Some more help or examples should have been provided for the programming exercises, especially the last one It was a well timed course with short videos.

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last one in 3 reviews

Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

State Coordinator 1 $53k

State $63k

State Surveyor- $64k

Department of State $72k

State Broker $75k

State police $80k

State Officer, State Exchanges $84k

Application Development Engineer - Fixed Robots $88k

Application Development Engineer - Fixed Robots $88k

Applications Support Engineer - Fixed Robots $104k

Applications Support Engineer - Fixed Robots $104k

State Treasurer $110k

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Coursera

&

University of Pennsylvania

Rating 3.5 based on 80 ratings
Length 5 weeks
Effort 4 weeks of study, 3-4 hours/week
Starts Jul 6 (7 days ago)
Cost $49
From University of Pennsylvania via Coursera
Instructor Daniel Lee
Download Videos On all desktop and mobile devices
Language English
Subjects Engineering Mathematics
Tags Physical Science And Engineering Mechanical Engineering Math And Logic

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