Year 12 — Software Automation

This module builds intuition for machine learning and automation through simple, reproducible Python examples, with a focus on from-scratch implementations to develop deep understanding of core algorithms and concepts.

Module Overview

Software automation represents the intersection of programming skills and intelligent systems. This module covers machine learning fundamentals, core algorithms, and their applications in automated systems, emphasizing both theoretical understanding and practical implementation.

Learning Outcomes

Students will develop the ability to:

• Distinguish between AI, ML, and automation approaches (RPA/BPA)

• Build and trace simple models conceptually and in code

• Implement core regression algorithms and neural networks from scratch

• Assess the impact of automation on individuals, society, and environment

• Evaluate bias and ethical considerations in ML/AI systems

Chapters

Implementation Philosophy

This module emphasizes practical understanding through implementation:

• From-scratch Python implementations to build algorithmic intuition

• Real-world examples demonstrating automation applications

• Mathematical foundations explained through working code

• Comprehensive evaluation and validation techniques

• Ethical considerations integrated throughout technical content

Prerequisites

• Solid Python programming skills

• Basic mathematical concepts (algebra, statistics)

• Understanding of data structures and algorithms

• Familiarity with problem-solving methodologies

Getting Started

1. Begin with Chapter 20 to establish ML and automation foundations

2. Progress through Chapter 21 for hands-on algorithm implementation

3. Complete with Chapter 22 to understand broader implications and ethical considerations

The module is designed for sequential progression, with each chapter building upon previous concepts while introducing new practical applications and implementation challenges.