Machine Learning for All Course Syllabus

Overview: This course provides an accessible, project-driven introduction to machine learning, designed for beginners with no programming background. You'll learn core ML concepts through concise videos, interactive browser-based tools, and real-world case studies. The course spans approximately 18 hours across four modules, combining theory, hands-on practice, and critical reflection on ethical implications. By the end, you’ll build and evaluate your own image-recognition model without writing code, and gain literacy essential for technical and non-technical roles alike.

Module 1: Machine Learning Basics

Estimated time: 5 hours

• Define artificial intelligence and distinguish it from machine learning
• Identify key problems that machine learning can solve
• Train a basic learning model using a no-code, browser-based tool from Goldsmiths
• Understand how algorithms learn patterns from data

Module 2: Data Features

Estimated time: 2 hours

• Explain how data is represented in digital form (bits and bytes)
• Describe different types of data and their use in ML
• Explore techniques for feature representation and selection
• Understand how features influence model performance and fairness

Module 3: Machine Learning in Practice

Estimated time: 5 hours

• Test and evaluate machine learning models using real-world datasets
• Identify opportunities and limitations of ML applications
• Analyze case studies of ML in industry and society
• Learn from expert interviews on practical implementation

Module 4: Your Machine Learning Project

Estimated time: 6 hours

• Collect and prepare a dataset for image recognition
• Train and test your own model using a no-code web tool
• Evaluate model performance and reflect on ethical considerations

Prerequisites

• Familiarity with basic computer operations
• No programming experience required
• Access to a modern web browser

What You'll Be Able to Do After

• Understand how machine learning models are trained on data without coding
• Explain how data features impact model outcomes and bias
• Use browser-based tools to build and test an image-recognition model
• Critically assess the societal benefits and risks of ML applications
• Communicate effectively about ML concepts in technical and non-technical settings