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AI for Students (AI4S)® (Hands-On: Mars Rover)

Overview

Course Instructor:  John ANG | Senior AI Engineer | AI Industry Innovation

1 December 2021
Dear Learners,
We have replaced the AI4S hands-on courses today (1st December 2021) to use Orange, a free and open-source data visualization, machine learning and data mining toolkit. These courses are available on the Students course catalog page.
Microsoft has announced the product end of line for Azure Machine Learning (Classic) for end August 2024. You can read this announcement here.
ML Studio (Classic) was used in our AI4E Hands-on courses. If you have created a ML Studio workspace prior to 1 Dec 2021, you can continue to use this platform until the product retirement. Alternatively, you can migrate to Azure Machine Learning service where the Designer feature is a direct replacement for ML Studio (Classic).
Learners who have previously completed this hands-on do not need to attempt these courses in order to maintain their badges or certificates.
Happy Learning!

Course Information

3...2...1...The Opportunity has successfully touched down on Mars! The Opportunity was a Mars Rover launched in 2003 to explore the Martian surface and its geology. The rovers landed on Mars in January 2004. The Opportunity was operational until 2018 (over 14 years!).

You are now in charge of a similar mission to Mars. Your rover is powered by solar panels, and learning from the previous rovers sent to Mars, the solar panels of your rover are only deployed when charging, to minimize the dust buildup on the solar panels and to prolong the rover's life.

Your challenge now: Create a model to determine the best time to deploy the solar panels for charging. Your team of scientists have analyzed the data from the previous rover missions, and have determined that the on Mars has a large effect on the amount of sunlight available to the solar panels. They have collected the following data over the previous rover missions - TEMPERATURE, PRESSURE, HUMIDITY, WIND DIRECTION and SPEED.

Unfortunately, there are no pre-existing models of weather on Mars that we can use. One of your scientists has suggested that you use Machine Learning to learn a model from the data. He says that Machine Learning may be able to help you get the job done much more quickly than trying to adapt a model from Earth weather to trying to use trial and error.

Learning Outcomes

You will get hands-on practice on creating a machine learning model to predict the best time to deploy the solar panels on the Mars Rover.

Structure Overview and Typical Completion Time

This course consists of 1 video tutorial.

The estimated course learning time is 1 hours.

Programme Support

For content questions and technical issues, please reach out to us at our Community Group

Current Status
Not Enrolled
Price
Closed
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