YOUR FOUNDATION-FIRST SYLLABUS
Recommender systems
Follow how a service chooses which items to show and in what order. Learn from patterns in earlier choices, then examine how showing an item changes the data the service sees next.
0 of 45 core ideas demonstrated45 lessons525 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 8–14 How computers represent a problem Explore everyday AI ideas; how AI got here; how computers represent a problem; and supporting ideas. 7 lessons · 0 of 7 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 explore14 The numbers you actually need Math preparation · 10 min
To exploreChapter 3 · Lessons 15–21 Finding patterns and useful representations Explore finding patterns and useful representations; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
15
Vectors: lists that work together Math preparation · 10 min
To explore16 Read an equation one symbol at a time Math preparation · 10 min
To explore17 Follow shapes through a numerical layer Math preparation · 10 min
To explore18 Directions, eigenvectors, and low-rank structure Math preparation · 10 min
To explore19 Probability without the mystery Math preparation · 10 min
To explore20 Averages over uncertain outcomes Math preparation · 9 min
To explore21 Compress variation into fewer directions Learning mechanisms · 13 min
To exploreChapter 4 · Lessons 22–29 Learning from useful data Explore learning from useful data; learning to predict; math preparation when it is needed. 8 lessons · 0 of 8 core ideas demonstrated
22
From the world to a dataset Learning mechanisms · 11 min
To explore23 A function is a rule Math preparation · 9 min
To explore24 Build a tiny prediction model Learning mechanisms · 11 min
To explore25 Powers and logarithms, one step at a time Math preparation · 11 min
To explore26 Turn a linear score into a class probability Learning mechanisms · 11 min
To explore27 Who is represented by the data? Learning mechanisms · 14 min
To explore28 Decide what success means before training Learning mechanisms · 12 min
To explore29 Look inside the average score Learning mechanisms · 14 min
To exploreChapter 5 · Lessons 30–37 Working with uncertainty Explore finding patterns and useful representations; working with uncertainty; learning from useful data; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
30
Estimate what a person may find useful Learning mechanisms · 14 min
To explore31 Probability, likelihood, and what is unknown Learning mechanisms · 11 min
To explore32 Update a belief using new evidence Learning mechanisms · 13 min
To explore33 Value a choice and its later consequences Math preparation · 12 min
To explore34 Turn a probability into a justified action Learning mechanisms · 11 min
To explore35 What can a small experiment tell us? Math preparation · 10 min
To explore36 Clean data without erasing the problem Learning mechanisms · 12 min
To explore37 Protect the examples used to judge a model Learning mechanisms · 15 min
To exploreChapter 6 · Lessons 38–45 Testing what a model has learned Explore learning to predict; testing what a model has learned; how computers represent a problem; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
38
Control complexity without peeking at the answer Learning mechanisms · 12 min
To explore39 Evaluate the procedure that chose the model Learning mechanisms · 14 min
To explore40 Change one thing and measure what follows Learning mechanisms · 13 min
To explore41 Repeat an experiment without confusing luck with truth Learning mechanisms · 11 min
To explore42 Make the result possible to inspect Learning mechanisms · 12 min
To explore43 Measure the behavior that the task actually needs Learning mechanisms · 14 min
To explore44 Decide whose problem the system solves and who bears its errors Learning mechanisms · 12 min
To explore45 Learn from choices that your system helped create Learning mechanisms · 13 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.Choose between known rewards and learning about alternativesRecommenders face a bandit problem every time they choose what to show: learn when to try something new and how to measure the cost of trying.Learn by exchanging messages across a graphRecommend through the social or purchase graph by passing messages between connected items.Ask what would change if an action changed the worldPrediction isn't intervention. Learn when a model can tell you what would happen if you acted.Notice when the production task changesYour model worked at launch. Learn to notice when the world changes under it.