Hands-On Exercise: Mars Rover
This course is a hands-on practice for students using Orange Data Mining
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.
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 presentation slide.
The estimated course learning time is 1 hour.
For content questions and technical issues, please reach out to us at our Community Group.