How to Teach ML

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Category	AI For Oceans	AI-Infused	AIThaiGen	AlpacaML	ANN	Any-Cubes	AppInventor	ArtBot RL	ArtBot SL	ARtonomous	AWS DeepRacer	AWS Simulator	BlockWiSARD	Brain in a Bag	BugBrain	Build a Neural Network	Calypso for Cozmo	Capture it!	CART	ChemAIstry	Child friendly	Chocolate Chips	ClassyTrashMonster	CODAP NetsBlox	Cognimates	Cognimates AI	Collaborative ML Model Building	Contours to Classification	CONVO	Cookbook	Crawling robot	CUHKiCar	danceON	Decision Tree Learning	Decision Tree Learning in Orange	DeepScratch	DoodleIT	Early Introduction of AI	ecraft2learn	eCraft2learn Snap!	Ethics and AI	Expected Goals	Food Decision Tree learning	GANs	GenderBias	Glyphs	Gold rush	Google Teachable Machine and Scratch	GTeach	Hexapawn	Images with Neural Networks	Industry 4.0 Robots	Interaction with black-box	Interactive Visualizations	It´s not Magic After All	JS-Eden	K-Means	K-MEANS Scratch	Lawn bowling	Learn Machine Learning	LearningML	LuminAI	Machine learning model in Scratch	Machine Learning Unplugged	Make me Happy	Mango	Maze	mBlock5.0	Micro:bit ScratchAI	Milo	Minecraft Education AI 4 Oceans	Minecraft Learns ML	Mini Impurity	ML with Candy	ML4K	ML-Machine	ML-Quest	Monkeys	Neural Network Playground	Neuron Sandbox	Neuron-based water system	Nim game	NLP4All	NN Scratch	Pasta-Land	Penguin-k-NN	Personal Image Classifier	Personalizing homemade bots	PlushPal	PopBots	PoseBlocks	PoseNet	Post-its	PRIMARYAI	Q-Learning Playground	Q-Learning Snap!	Qube	Recycling problem	Reinforcement Learning Activity	Robobo SmartCity	Role Playing Game	SAILORS	ScratchML	ScratchML4K	Scratch-NB	Semantic networks	Sign Language	Smart Bin	SmileyCluster	Snap!	Sports and Machine Learning	StoryQ	Super Meat Bot	Teachable Machine	Teachable Machine Primary	TensorFlow Playground	Text Classifier	The Personal Image Classifier	The Popstar	Tic Tac Toe	Tooee	TryColors	VotestratesML	WiSARD	WoZ-based	Zhu´s MIT App Inventor
Reinforcement Learning An agent learns by acting in the environment and adjusting its policy from reward or punishment.								x		x	x	x																			x																			x		x			x	x			x								x					x					x					x						x							x	x	x		x							x							x							x
Unsupervised Learning Algorithms discover patterns or structure in un-labelled data.																						x																								x	x										x	x				x		x						x																							x					x				x							x
Supervised Learning The model is trained on labeled data and learns to predict the output for new inputs.	x	x	x	x	x	x	x		x				x			x	x	x	x	x	x		x	x	x	x	x	x	x	x		x	x	x	x	x	x	x	x	x	x	x	x		x			x	x		x		x	x						x	x		x		x	x		x	x		x		x	x	x	x		x	x	x			x		x	x	x		x	x	x	x		x						x	x	x	x	x	x		x	x		x	x	x		x	x	x	x	x	x	x	x	x	x	x	x	x
Machine The learner embodies the algorithm. The learner perform each step exactly as a computer would.														x								x						x						x	x							x		x						x														x			x							x			x					x			x	x													x		x
User Learners interact with the model (e.g. giving labels) but do not modify its internal logic.	x	x	x	x		x	x			x			x				x	x	x	x	x		x	x	x	x	x			x	x	x	x	x		x	x	x	x	x	x			x	x			x	x	x		x	x	x					x	x	x	x	x			x	x	x	x		x				x	x					x	x	x				x	x	x	x	x	x		x					x	x	x		x	x	x	x	x	x		x	x	x		x	x		x	x	x		x	x	x		x	x
Creator Learners design or implement substantial parts of the algorithm.					x			x	x		x	x			x									x					x						x								x			x	x				x				x	x	x	x		x						x						x	x					x	x	x				x											x	x	x	x			x	x							x				x			x				x
Invisible Use The user uses the algorithm, with no transparency to the internal processing or mechanisms.	x		x	x		x	x													x			x			x	x			x		x						x	x	x	x				x				x				x	x					x	x	x	x						x	x						x	x							x				x	x	x	x	x	x												x		x	x				x		x	x	x			x				x			x
Button The user activates the algorithm through a simple interface action (e.g., clicking a button), but the internal algorithmic operations remain hidden.			x	x		x	x													x			x			x	x			x								x	x	x	x							x	x				x	x					x	x	x	x			x			x	x						x	x							x				x				x	x												x						x			x	x	x			x
Model Deployment The user integrates and applies the trained model directly into code without needing access to the inner workings of the algorithm.		x		x			x						x				x	x			x				x	x				x			x			x		x	x	x								x				x									x		x		x			x	x		x				x												x				x			x						x			x	x	x			x		x							x	x	x		x					x
Return Value The user interacts with the model and receives a return value.				x																					x	x										x		x														x									x		x		x						x				x												x													x				x	x	x		x		x	x								x		x					x
View The user can transparently observe each step of the algorithm's operation, providing clear insight into its internal processes.										x	x	x							x												x			x			x																																																																																							x
Execute The user explicitly executes the algorithm step-by-step, actively participating in the procedural logic to achieve the solution.														x		x			x			x						x						x	x							x	x	x						x	x													x			x						x	x			x	x				x			x	x							x						x		x																							x
Parameter The user can manipulate algorithm-specific parameters to influence the behavior of the algorithm, and then re-run the modified algorithm.								x	x		x	x																						x	x											x										x										x						x								x	x														x		x	x											x			x				x							x
Edit The user completes or structures parts of the algorithm with substantial guidance, such as provided scaffolding, code snippets, or sequence rearrangements.					x																			x																							x								x		x	x												x									x					x												x					x	x																		x
Create The user independently develops the entire algorithm or substantial parts of it without structured assistance or detailed guidance.															x														x																															x
Data Collecting, cleaning, or transforming raw data prior to training or testing a model.	x	x	x	x		x	x		x				x			x		x	x		x	x	x	x	x	x	x	x	x	x		x	x		x	x	x	x	x	x	x			x		x		x	x		x	x	x					x		x	x		x	x	x	x		x	x		x		x		x	x		x					x	x			x	x	x		x	x		x				x			x	x	x	x	x	x		x	x	x	x		x	x	x	x	x	x				x		x	x
Test Check model performance on data.	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x		x		x	x	x		x	x	x	x	x	x	x	x	x	x		x	x	x		x	x	x	x	x		x	x	x	x	x	x				x	x	x	x	x	x	x	x	x	x	x	x		x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x		x	x		x
Block-oriented Drag-and-drop, visual-block environments (Scratch, Snap!, Blockly) are used to build, train or use the model.		x		x	x		x						x				x	x			x			x	x	x				x						x		x	x	x							x	x							x		x				x		x		x			x	x	x	x				x				x								x				x			x		x				x			x	x	x			x		x							x	x	x		x
Unplugged The algorithm is enacted without computers.														x		x						x													x							x	x	x						x	x													x			x						x	x				x				x			x	x							x						x		x					x
Plugged Learners work with a text-based coding environment or GUI on a computer.	x	x	x	x	x	x	x	x	x	x	x	x	x		x		x	x	x	x	x		x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x				x	x	x	x	x			x	x	x	x	x	x	x	x	x	x	x	x		x	x		x	x	x	x	x			x	x	x		x	x	x		x	x			x	x	x	x	x	x		x	x	x	x	x		x		x	x	x	x		x	x		x	x	x	x	x	x	x	x	x	x	x	x	x	x	x	x
Neural Network Layered artificial neurons whose weights are repeatedly adjusted.					x									x	x													x									x							x							x																			x									x	x	x			x																														x						x				x

Learning Method

User Role for the Algorithm

Levels of Algorithmic Abstraction

Further information