YOUR FOUNDATION-FIRST SYLLABUS
Time-series forecasting
Use earlier observations to predict what may happen next. Compare a simple starting guess with a learned model, keep future information out of training, and check errors at different time horizons.
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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 14–20 Learning from useful data: From the world to a dataset Explore learning from useful data; learning to predict; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
14
From the world to a dataset Learning mechanisms · 11 min
To explore15 The numbers you actually need Math preparation · 10 min
To explore16 A function is a rule Math preparation · 9 min
To explore17 Build a tiny prediction model Learning mechanisms · 11 min
To explore18 Probability without the mystery Math preparation · 10 min
To explore19 Averages over uncertain outcomes Math preparation · 9 min
To explore20 What can a small experiment tell us? Math preparation · 10 min
To exploreChapter 4 · Lessons 21–27 Learning from useful data: Clean data without erasing the problem Explore learning from useful data; working with uncertainty; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
21
Clean data without erasing the problem Learning mechanisms · 12 min
To explore22 Protect the examples used to judge a model Learning mechanisms · 15 min
To explore23 Who is represented by the data? Learning mechanisms · 14 min
To explore24 Decide what success means before training Learning mechanisms · 12 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 explore27 Sets, graphs, and counting possibilities Math preparation · 10 min
To exploreChapter 5 · Lessons 28–34 Working with uncertainty Explore working with uncertainty; learning to predict; testing what a model has learned; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
28
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 Powers and logarithms, one step at a time Math preparation · 11 min
To explore31 Turn a linear score into a class probability Learning mechanisms · 11 min
To explore32 Control complexity without peeking at the answer Learning mechanisms · 12 min
To explore33 Evaluate the procedure that chose the model Learning mechanisms · 14 min
To explore34 Predict the future without borrowing information from it Learning mechanisms · 14 min
To exploreOptional extensions and comparisons (7)
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.Carry a state from one step to the nextCarry a memory through a time series step by step: the classic neural forecaster.Represent a sequence through an evolving hidden stateThe state-space idea behind Mamba, and a classic tool for forecasting.Learn distributions over plausible explanationsForecasts with honest error bars: keep several plausible explanations instead of betting on one.Use fixed recurrent dynamics and train a readoutA fixed random network plus a trained readout: surprisingly strong for some time series.Notice when the production task changesYour model worked at launch. Learn to notice when the world changes under it.