Machine Learning with Python


Machine Learning using Python

Year level 10

Summary

This is for students who are have experience in Python programming, and are able to independently write their own programs.

Suggested steps

  1. Watch this video by CGP Grey for a general understanding on how machines learn
  2. Unplugged Activity: Students are given half of the Iris flower data set.
    1. They work in pairs to figure out how they could determine the species if they are given the data for a new flower (without a computer).
    2. Then, they compete in order to successfully label 10 flowers that they have not seen in their dataset. The team with the highest success rate wins a prize.
  3. Watch the first video in this AMAZING playlist by 3Blue1Brown to learn how neural networks can be used for machine learning
  4. Students to followthis tutorial about using machine learning in Python to predict flowers based the famous iris dataset.
  5. Students to find or create another data set for classification, and apply the tutorial to that data set.

Discussion

  • How well did your classification Python program predict the labels for your dataset?
  • How easily could you predict the labels yourself as a human?
  • What could make this classification task “easier” for the computer? (What extra information could you provide?)

Why is this relevant?

[As a result of doing this task how will student’s knowledge of Digital Technologies concepts be developed further? Describe reasons why this task is important making connections to Digital Technology concepts. Include relevance to real world applications and link to relevant concepts such as Algorithms, Digital Systems and/or data representation for example]

This task provides concrete practise in using a classification algorithm on a dataset (and analysing whether it worked). This is important both for the Algorithms and Data Representation (as students can learn how a neural network is used to represent a “computer brain” in machine learning)

Assessment

  • Prepare a screencast to document your Python script, the dataset you are using, and how well it predicted the outcome

Curriculum links

Links with the Digital Technologies curriculum area

                       

Year band

Strand Content description
Years 9-10 Processes and Production Skills Evaluate critically how student solutions and existing information systems and policies, take account of future risks and sustainability and provide opportunities for innovation and enterprise (ACTDIP042)
Years 9-10 Knowledge and Understanding Develop techniques for acquiring, storing and validating quantitative and qualitative data from a range of sources, considering privacy and security requirements (ACTDIP036 – Scootle)

 

ADD Links with other curriculum areas

                       

Year band

Learning area Content description
Year 11-12 (SACE) Digital Technologies Advanced Programming

Data Analysis

 

+ There are no comments

Add yours