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AI Maker CourseAI Maker Course with Teachergoogle teachable machineface recognition on Una Web App

AI Maker 2.0

IT STEM Standard 10 10 hrs AI Block Coding

This course introduces the fundamentals of Artificial Intelligence (AI), including its applications, ethical implications, and practical techniques. Gain hands-on experience with AI technologies like Microsoft Azure and Google Teachable Machine, and create your own AI web apps. Develop a solid understanding of AI principles, techniques, and applications, prepare to explore more advanced AI topics and projects.

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Learning objectives

  • Grasp essential AI concepts and their applications in daily life.
  • Learn to build and train AI models with hands-on tools.
  • Create a simple AI-powered web app from scratch.

Lesson plan

Introduction to AI | AI in daily life and common stereotypes of AI, opened with a discussion of first impressions; "Does it have AI?" walk-through of everyday products such as Google Translate (translation, speech-to-text, read-aloud).
Object detection | "Finding Scruffy" warm-up, then computer vision as a field: pictures are just numbers to a computer. Surveys classification, object detection, semantic segmentation, describe-image, face detection and OCR, then focuses on object detection returning bounding-box coordinates.
Image Classification | Colour-grouping warm-up, then categorising images by content and the full training flow: define classes, prepare data per class, train, test.
Face AI | Task 1 face detection (finding faces in pictures, camera autofocus, counting people entering an area). Task 2 face verification: detection extracts facial features from about 80 nodes on the face, then two faces are compared with a mathematical model.
Pose Detection | Video warm-up on motion capture, then finding people in an image and matching the pose to the most similar action by locating up to 17 skeleton joints (eyes, shoulders, knees).
Speech | Homophone warm-up, machine translation, direct versus contextual translation, speech synthesis as text analysis then linguistic analysis then audio generation, and speech translation. Ends with a hands-on real-time speech translation demo recorded through a microphone.
Generative AI | Spot-the-AI-image warm-up, what generative AI is, NLP capabilities (speech, text analytics, translate, chatbot), traditional knowledge-base chatbots compared with GPT, then hands-on with ChatGPT via poe.com for knowledge mining and key-phrase extraction, plus how to steer output with prompts.
AI, how do you learn? | Number-pattern challenge, then machine learning, data processing and classification: supervised versus unsupervised learning, classification versus regression, training data, plotting data and simulating a regression training process. Supported by the bundled ML Example Data sets (Diabetes, Titanic, Boston House Price, Student Grade, Student Stress Factors).
Is AI invincible? | Ethics of AI through real cases: biased training data producing unjust analysis (age, gender, skin colour), wrongful use for fraud and identity theft, and over-reliance on unvalidated AI output. Closes on AI as a tool whose outcome depends on the user.
Make your own app | Build a web app: screens and layouts, then layouts nested inside layouts, then Task 3 "AI Maker" wiring a Google Teachable Machine model (apple versus orange) into the Una WebApp. Ends with a knowledge check.
AI Maker project: design your own AI product | Recap of object detection, face detection and training your own model, then design an AI product for a real-life problem (health, environment, history, equality), sketch the idea and present it.

Prerequisites

  • None

Software

  • Una Virtual Classroom - Advanced

Other requirements

    Hardware

    • Notebook/Desktop with a webcam and the updated browser is recommended.
    • iPad does not support training AI models with Google Teachable Machine.

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