YOUR FOUNDATION-FIRST SYLLABUS
Robotics
Start with a robot trying to reach a target. Follow how it measures its surroundings, plans a move, acts, and uses new measurements to adjust. Learning actions from examples and autonomous driving are further topics.
0 of 55 core ideas demonstrated55 lessons680 estimated minutes remaining
Already know some of this?
Go straight to an idea’s knowledge check. Passing its different checks without hints carries that evidence into this path. Checking an advanced idea does not award its prerequisites; you can still explore them.
Check what I already knowTHE IDEAS, IN ORDER
Shared credit is automaticBuild your understanding.
Chapter counts use compatible knowledge-check evidence. Reading a lesson does not mark its ideas as demonstrated.
Chapter 1 · Lessons 1–7 Meet an AI system Start with an everyday task, then follow its inputs, model, and output. 7 lessons · 0 of 7 core ideas demonstrated Up next
01
What is AI? Start with an everyday task Common Ground · 11 min
To explore02 Inputs and outputs: what goes in and what comes out Common Ground · 13 min
To explore03 Data and examples: what a computer can learn from Common Ground · 10 min
To explore04 What is a model? A small rule inside a bigger app Common Ground · 10 min
To explore05 Weights: the adjustable numbers in a model Common Ground · 12 min
To explore06 Training and inference: changing a rule or using it Common Ground · 13 min
To explore07 Follow one small AI system from start to finish Common Ground · 9 min
To exploreChapter 2 · Lessons 8–13 How computers represent a problem Explore everyday AI ideas; how AI got here; how computers represent a problem. 6 lessons · 0 of 6 core ideas demonstrated
08
Different ways to solve the same problem Common Ground · 11 min
To explore09 How to tell whether an AI answer is useful Common Ground · 10 min
To explore10 The questions that shaped AI Common Ground · 15 min
To explore11 How a computer stores a picture or a sentence Learning mechanisms · 16 min
To explore12 Describe a computation so someone else can follow it Learning mechanisms · 11 min
To explore13 What a representation makes easy to learn Learning mechanisms · 12 min
To exploreChapter 3 · Lessons 14–19 Connecting models to physical actions Explore connecting models to physical actions; math preparation when it is needed. 6 lessons · 0 of 6 core ideas demonstrated
14
The numbers you actually need Math preparation · 10 min
To explore15 Vectors: lists that work together Math preparation · 10 min
To explore16 Distances, neighborhoods, and transformations Math preparation · 12 min
To explore17 Connect measurements and actions to a physical body Learning mechanisms · 14 min
To explore18 A function is a rule Math preparation · 9 min
To explore19 Read an equation one symbol at a time Math preparation · 10 min
To exploreChapter 4 · Lessons 20–26 Working with uncertainty: Probability, likelihood, and what is unknown Explore connecting models to physical actions; working with uncertainty; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
20
A derivative is a local change Math preparation · 13 min
To explore21 Follow a changing state through time Math preparation · 12 min
To explore22 Correct motion using measured error Learning mechanisms · 13 min
To explore23 Probability without the mystery Math preparation · 10 min
To explore24 Averages over uncertain outcomes Math preparation · 9 min
To explore25 Probability, likelihood, and what is unknown Learning mechanisms · 11 min
To explore26 Update a belief using new evidence Learning mechanisms · 13 min
To exploreChapter 5 · Lessons 27–33 Working with uncertainty: Factor a joint distribution into smaller pieces Explore working with uncertainty; connecting models to physical actions; searching and planning; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
27
Sets, graphs, and counting possibilities Math preparation · 10 min
To explore28 Factor a joint distribution into smaller pieces Learning mechanisms · 11 min
To explore29 Track a hidden state from noisy observations Learning mechanisms · 12 min
To explore30 Estimate where the robot is while learning the map Learning mechanisms · 14 min
To explore31 Turn a goal into states and actions Learning mechanisms · 13 min
To explore32 Reason with statements that are true or false Learning mechanisms · 13 min
To explore33 Remove impossible choices before searching further Learning mechanisms · 14 min
To exploreChapter 6 · Lessons 34–40 Learning from useful data Explore connecting models to physical actions; learning from useful data; learning to predict; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
34
Turn a desired pose into feasible movement Learning mechanisms · 15 min
To explore35 From the world to a dataset Learning mechanisms · 11 min
To explore36 Build a tiny prediction model Learning mechanisms · 11 min
To explore37 Powers and logarithms, one step at a time Math preparation · 11 min
To explore38 Turn a linear score into a class probability Learning mechanisms · 11 min
To explore39 Who is represented by the data? Learning mechanisms · 14 min
To explore40 Decide what success means before training Learning mechanisms · 12 min
To exploreChapter 7 · Lessons 41–47 Learning to predict Explore learning to predict; working with uncertainty; learning from useful data; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
41
Look inside the average score Learning mechanisms · 14 min
To explore42 Value a choice and its later consequences Math preparation · 12 min
To explore43 Turn a probability into a justified action Learning mechanisms · 11 min
To explore44 What can a small experiment tell us? Math preparation · 10 min
To explore45 Clean data without erasing the problem Learning mechanisms · 12 min
To explore46 Protect the examples used to judge a model Learning mechanisms · 15 min
To explore47 Control complexity without peeking at the answer Learning mechanisms · 12 min
To exploreChapter 8 · Lessons 48–55 Testing what a model has learned Explore testing what a model has learned; how computers represent a problem; checking reliability and behavior; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
48
Evaluate the procedure that chose the model Learning mechanisms · 14 min
To explore49 Change one thing and measure what follows Learning mechanisms · 13 min
To explore50 Repeat an experiment without confusing luck with truth Learning mechanisms · 11 min
To explore51 Make the result possible to inspect Learning mechanisms · 12 min
To explore52 Measure the behavior that the task actually needs Learning mechanisms · 14 min
To explore53 Decide whose problem the system solves and who bears its errors Learning mechanisms · 12 min
To explore54 Evaluate closed-loop behavior under physical uncertainty Learning mechanisms · 13 min
To explore55 Project: trace a robot sensing and control loop Your system, connected · 35 min
To exploreOptional extensions and comparisons (6)
These lessons deepen or compare the reference design. They do not add requirements to this path’s completion.
How to use this school without getting lostOptional orientation: how paths, side lessons, and practice fit together, plus study habits that make learning stick.Practice, progress, and knowing what you understandOptional orientation: use practice, mistakes, and spaced review to find out what you really understand.Learn physical actions from demonstrationsTeach a robot by demonstration, and learn why small errors snowball.Learn in simulation and test what transfersTrain robots in simulation, then cross the gap to the real world.Connect visual instructions to a robot’s action interfaceRobots that follow spoken instructions: connect vision and language to motor actions.Predict what may happen next, with or without actionsModels that predict what happens next, for planning and generation.