Course and AIMA-aligned sequence
Use this as the default full-course order. It begins with state-space search, moves through logical and probabilistic reasoning, then develops learning, representation, vision, and reinforcement learning.
Curriculum Map
The suite supports two different orders. The course-aligned sequence preserves conceptual dependencies and follows the broad AIMA progression from search to logic, probability, machine learning, and reinforcement learning. The quick-entry sampler is optimized for a first visit, not for teaching the full course in order.
Quick-entry sampler
Use this for demonstrations, open houses, or a four-lesson introduction. It deliberately jumps across the field to create four immediate and contrasting experiences.
Quick-entry four-app sampler
Visible comparison of BFS and A*.
A memorable base-rate reversal.
Training success versus generalization.
Trial and error producing a policy.
Why this is not the course order: the sampler maximizes variety and first-visit impact. It skips prerequisite development and should not replace the full sequence below.
Course and AIMA-aligned applet sequence
This sequence matches the structure used to build the course and the broad ordering of foundational topics in Artificial Intelligence: A Modern Approach. It is an instructional alignment, not a chapter-by-chapter reproduction.
Pathfinding VisualizerHill Climbing and Simulated AnnealingWumpus WorldCNF and SAT BuilderBayes Rule PlaygroundBayesian NetworkK-Nearest NeighborsOverfitting ExplorerTiny Neural NetworkK-Means ClusteringConvolution PlaygroundQ-Learning Gridworld
| # | Applet | Concept area | Why it appears here |
|---|---|---|---|
| 1 | Pathfinding Visualizer | Search and problem solving | Introduce controlled exploration, optimality, and heuristic focus. |
| 2 | Hill Climbing and Simulated Annealing | Search and optimization | Show why local improvement can stall and why escape strategies matter. |
| 3 | Wumpus World | Logic and knowledge | Bridge perception, inference, uncertainty, and safe action. |
| 4 | CNF and SAT Builder | Logic and knowledge | Formalize propositional reasoning and search over assignments. |
| 5 | Bayes Rule Playground | Probability | Surface base-rate neglect before moving to graphical models. |
| 6 | Bayesian Network | Probabilistic reasoning | Extend conditional probability into dependence and explaining away. |
| 7 | K-Nearest Neighbors | Machine learning | Introduce supervised classification through visible neighborhood votes. |
| 8 | Overfitting Explorer | Machine learning and evaluation | Separate training performance from generalization on new data. |
| 9 | Tiny Neural Network | Neural networks | Build intuition for hidden representations and nonlinearity. |
| 10 | K-Means Clustering | Unsupervised learning | Introduce iterative unsupervised clustering and initialization sensitivity. |
| 11 | Convolution Playground | Computer vision | Connect local filters to feature maps and learned visual representations. |
| 12 | Q-Learning Gridworld | Reinforcement learning | Conclude with trial-and-error learning, delayed reward, and policy emergence. |
Applet map
Each card uses the same accent color as its applet, so the concept remains visually recognizable across the landing page, Teacher Pack, and curriculum materials.
Search and problem solving
Pathfinding Visualizer
Time: 20 min
Compare BFS, DFS, Dijkstra, and A* on the same grid. Inspect path cost, explored nodes, and frontier behavior.
Classroom use: Introduce controlled exploration, optimality, and heuristic focus.
Core question: Why does A* usually expand fewer nodes than BFS when the heuristic is useful?
Search and optimization
Hill Climbing and Simulated Annealing
Time: 25 min
Run greedy ascent and simulated annealing from the same landscape and compare their escape options.
Classroom use: Show why local improvement can stall and why escape strategies matter.
Core question: When can a worse local step produce a better final result?
Logic and knowledge
Wumpus World
Time: 25 min
Follow a logical agent as percepts become safe-cell conclusions, unresolved risks, and forced choices.
Classroom use: Bridge perception, inference, uncertainty, and safe action.
Core question: What is the difference between an unknown square and a square proven safe?
Logic and knowledge
CNF and SAT Builder
Time: 25 min
Rewrite propositions into conjunctive normal form and inspect how DPLL prunes assignments.
Classroom use: Formalize propositional reasoning and search over assignments.
Core question: Why do solvers prefer a standardized clause structure before search begins?
Probability
Bayes Rule Playground
Time: 20 min
Change the base rate and test quality to see why high accuracy can still produce a surprising posterior.
Classroom use: Surface base-rate neglect before moving to graphical models.
Core question: Why can a highly accurate test still produce many false alarms when the event is rare?
Probabilistic reasoning
Bayesian Network
Time: 30 min
Edit evidence, compare exact inference with sampling, and see explaining away in a causal graph.
Classroom use: Extend conditional probability into dependence and explaining away.
Core question: Why can evidence for one possible cause lower belief in another?
Machine learning
K-Nearest Neighbors
Time: 20 min
Move a query point, change k and vote weighting, and inspect every neighbor that determines the class.
Classroom use: Introduce supervised classification through visible neighborhood votes.
Core question: What does k control, and why can both small and large k fail?
Machine learning and evaluation
Overfitting Explorer
Time: 25 min
Raise model complexity and compare training error with test error on fresh samples.
Classroom use: Separate training performance from generalization on new data.
Core question: Why can a model that fits the training data perfectly fail on new data?
Neural networks
Tiny Neural Network
Time: 30 min
Train a compact network and compare linear composition with a nonlinear hidden representation.
Classroom use: Build intuition for hidden representations and nonlinearity.
Core question: How do hidden units and nonlinearity change what a classifier can separate?
Unsupervised learning
K-Means Clustering
Time: 25 min
Change k and initialization, then watch assignment and center updates reshape the clusters.
Classroom use: Introduce iterative unsupervised clustering and initialization sensitivity.
Core question: What changes during each iteration, and when should the algorithm stop?
Computer vision
Convolution Playground
Time: 30 min
Slide kernels across an image, inspect each multiply-and-sum, and compare hand-built with learned filters.
Classroom use: Connect local filters to feature maps and learned visual representations.
Core question: Why can a small matrix reveal edges, blur, or sharpen an image?
Reinforcement learning
Q-Learning Gridworld
Time: 30 min
Train by trial and error, inspect value backups, and compare exploration schedules on the same world.
Classroom use: Conclude with trial-and-error learning, delayed reward, and policy emergence.
Core question: How does useful behavior emerge from repeated trial and error?
Assessment evidence
Predict and observe
Capture initial intuition, then cite a visible state, value, trace, or decision after running the experiment.
Explain and transfer
Name the mechanism that produced the result, then apply the idea to another AI system or decision.