Prepare to Teach 'Software Automation' in Software Engineering

A facilitated online workshop series to support teacher confidence in the delivery of Software Automation. Teachers will commit to an eight-week series of one-hour online workshops, each with 1 hour of preparation work each week.

At the end of the workshop series, teachers will

1. Have a deeper understanding of the Machine Learning Algorithms listed in the syllabus and the underlying principles of Machine Learning.
2. Be able to interpret and modify practical OOP implementations of the Algorithms.
3. Have a practical understanding of MLOps.
4. Be provided with all resources to adapt or adopt to their context.

Teachers should be prepared to complete one hour of self-directed professional learning before the online workshop each week. The online workshop will focus on deepening understanding of the preparation work and practical application of the concepts through OOP Python implementations.

Session Times

The online workshops will begin week 1, term 2 until week 8, Tuesdays from 3:30-4:30pm.

Program

Note

This Program is indicative and will be adjusted as we progress, as some topics may take less time than planned and others longer.

Week	Overview	Workshop Outline	Preperation
0			Video's & Notes Clone Repo Create 'Codespace on main' Optional Google Introduction to ML
1	Linear Regression	Jupyter Notebooks Numpy & Pandas Linear Regression Linear Regression algorithm Intuition: Fit, underfit, overfit	Video's & Notes Problem Set
2	Complex Regression & Classification	OOP Multifeature Linear Regression OOP Polynomial Linear Regression Classification Logistic Regression algorithm OOP Logistic Regression	Video's & Notes Problem Set OOP Logistic Regression Iris Classification Problem
3	Data in Machine Learning	Bias V Variance Bias in Machine Learning Matplotlib One hot encoding	Video's & Notes Problem Set
4	Advanced Supervised Machine Learning	OOP K Nearest Neighbour OOP Decision Trees OOP Neural Networks	Video's & Notes Problem Set OOP Neural Network Iris Classification
5	MLOPs (Design)	MLOps Refactor machine learning problem Define success metrics	Video's & Notes Problem Set
6 & 7	MLOPs (Development)	Data wrangling Feature Engineering Model Fitting Testing and evaluation Model refining	Model development
8	MLOPs (Operations)	Model exporting Implement a CLI to a model	Capstone Project

Capstone Project

At the end of the workshop, teachers will be given an optional task that will require them to identify a data set, wrangle the data, engineer features, develop an ML model and put it into operations.

The optional task will be reviewed informally as a 1:1 online discussion about the final solution you have delivered anytime after the workshops have ended (no formal submission date).

Other Resources

1. Pre-configured Code Spaces DevContainer for Machine Learning - A repository template for Jupyter Notebook Python Development in GitHub CodeSpaces including image packages.
2. NESA Course Specification Linear Regression Implementation - A Jupyter Notebook collection designed to support students' understanding of the Linear Regression model defined in the NESA Software Engineering Course Specifications pg 28.
3. NESA Software Engineering - Machine Learning OOP Implementation Examples - A Jupyter Notebook collection designed to support students implement Programming for automation in the NESA Software Engineering Syllabus specifically using an OOP to make predictions.
4. Practical Application of NESA Software Engineering MLOps - A Jupyter Notebook collection designed to develop a practical understanding of Machine Learning Operations (MLOps) defined in the NESA Software Engineering Course Specifications pg 27.

External links:

• AI Fairness Project - Detect and mitigate bias in machine learning models.
• Machine Learning University - A collection of explainations and interactive visualisations about Machine Learning.
• FastAPI for ML Project - An ML project to build a simple API for a machine learning model.
• CICD for ML Project - An ML project to implement a CI/CD pipeline.
• Automate ML Testing Project - An ML project to automate a testing pipeline.
• Deploy an LLM Application with Docker - An ML project to build and deploy a LLM model in a docker container.

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Prepare to Teach Software Automation Professional Learning Resources by Ben Jones is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International