Artificial Intelligence for kids

Explore how AI powers Google search predictions, YouTube recommendations, and AI bots in Fortnite, illustrating AI's wide range of applications discussed in this lecture.

Explore the branches of artificial intelligence, including natural language processing and machine learning, with examples like sun position prediction and license plate recognition.

Create a zombie enemy by duplicating the player, spawn it at random positions, and have it move toward the player; bullets destroy the zombie and stop the game on contact.

Explore machine learning, a branch of artificial intelligence, and learn how a machine learns. Watch a short animation to understand its role and applications.

Learn how machines learn by analyzing data, mirroring how humans gain knowledge from experience. See how data trains algorithms to predict weather and enable features like photo filters.

Explore rock, paper, scissors in two modes: random mode and AI mode that learns your play style, then compare outcomes by trying both modes.

Explain the three types of machine learning: supervised learning uses labeled input-output pairs; unsupervised learning forms clusters; reinforcement learning trains an agent through rewards and penalties.

Create a machine learning project for kids by guiding them through simple steps to design and test.

Build a rock, paper, scissors game that recognizes your hand gesture with a machine learning model and webcam, then play against a computer that makes a random move.

Explore the basics of supervised learning as part of a kid-friendly introduction to artificial intelligence, with a short animation that explains how supervised learning fits with unsupervised and reinforcement learning.

Learn how supervised learning teaches machines from input data and correct outputs through examples, evaluation, and new data to predict weather and distinguish pens from pencils.

Participate in an AI activity that detects objects with a camera and translates their names into any language you choose, such as Spanish, with the option to switch languages.

Train a model on numerous pictures of objects to recognize and label new images, demonstrating supervised learning with labeled outputs like bullpen.

Compare classification and regression in supervised learning, noting classification outputs belong to lion or tiger categories. Regression outputs are numerical and can be a value, such as a shirt price.

Create a smart classroom project in Scratch three that uses machine learning blocks to recognize commands like fan on, fan off, lamp on, and lamp off.

Explore classification in supervised learning within machine learning, as this class explains how classification differs from regression and previews an animation that clarifies what classification is.

Explore how supervised learning classifies data to power object detection, handwriting recognition, and speech recognition in daily apps, from face detection for filters to handwriting input and waste sorting.

Explore the hand track activity by testing images and the webcam; see how the software detects hands, even while you move them, and learn how it works.

Explain how a hand recognition model trained on diverse shapes and positions identifies hands in live camera images and highlights them when the predicted confidence meets your threshold.

Explore the confidence threshold in artificial intelligence and how a model outputs a confidence percentage. See how a 70% threshold controls when the model answers, risking rejection of correct outputs.

Classify images of cats, dogs, and ducks by creating a project, labeling ten images per class, training a new machine learning model, and testing predictions with Scratch and visualizing results.

Explore text classification and how chatbots use machine learning to understand words and phrases, classify them into predefined classes, and automatically answer questions across customer service.

Engage with the Kooky chat bot to practice dialogue by saying hi and how you feel, then push its limits with long, confusing questions to test its responses.

Explore sentiment analysis through machine learning, teaching computers to understand human emotions by classifying words into positive, neutral, and negative scores to determine overall sentiment.

Students build a chat bot, label topics such as foods, countries, lifespan, species, and sizes, and train it to answer questions across these categories.

Define artificial intelligence as a computer brain that reasons and solves problems, and outline machine learning and natural language processing, including supervised, unsupervised, and reinforcement learning with classification and regression.

students apply image recognition with Scratch 3 to sort fruits and bottles into fridge sections in this mid course project, using confidence filtering and a two-label dataset.

Explore what data is and how to use it, and learn why a proper dataset is essential for building a reliable machine learning model.

Explore what data is in machine learning, from observations and measurements to images, videos, and text. Learn how data sets and variables, plus training–testing splits, shape model accuracy.

Learn regression as a supervised learning method to predict video game prices using independent variables like year of published, hygiene rating, and online mode, with price as the dependent variable.

Explore linear regression with a height versus shoe size dataset, identify the best fit line, interpret the correlation coefficient, and analyze residuals using custom data.

Learn how decision trees fit into machine learning as a method for predicting drawings and classifying images. Watch a short animation video that explains what decision trees are.

Learn how a decision tree uses a sequence of questions about weight and cylinders to predict a car's high or low range, shown through a simple example.

Explore how decision trees guide everyday choices, from deciding to go out based on friends and weather, to game bot decisions and business decisions, making outcomes more visual.

Interact with a decision-tree app in child mode to guess the object you have in mind by answering questions. Press play and use objects to guide the originator's questions.

Train an AI bot to play tic tac toe by collecting training data from your moves, building a model, and adjusting computer behavior for smarter play.

Learn how a decision tree starts at the root node, progresses through internal nodes to leaf nodes, with branches linking them, and how its size varies by problem.

Interact with a simple decision tree to explore preset samples, like the cars tree. See how answers guide the chosen option and how to edit the pattern.

Discover pruning in decision trees: remove low-value branches to reduce size and improve accuracy. Compare post pruning, removing branches after building tree, with pre pruning, which stops branching early.

Program Pac-Man to beat the ghost using a decision tree trained on Pac-Man and ghost coordinates via Scratch.