4.0 Study Guide and Task Sheet: AI Math Foundations
The main study route is now in Chapter 4 entry. Use this page only as a quick checklist while you practice.
One-Line Mental Model
represent data -> measure uncertainty -> measure loss -> update parameters
If a formula feels difficult, first ask what model action it supports.
Practice Checklist
| Check | Evidence |
|---|---|
| I can explain vector similarity | cosine similarity example |
| I can explain a matrix as data or transformation | small matrix note |
| I can simulate probability or uncertainty | probability output |
| I can explain entropy or loss in plain language | one concept card |
| I can trace gradient descent step by step | update table |
| I can finish the final workshop after theory | ch04_math_workshop_evidence/ |
Formula-To-Code Checks
| Idea | Concrete check |
|---|---|
| Vector | Label each dimension before calculating similarity. |
| Probability | Name the random variable, possible outcomes, and one event. |
| Loss | Compute one loss value by hand, then match it with code. |
| Gradient | Show one parameter before and after an update step. |
| Learning rate | Try one smaller and one larger value, then explain the loss curve. |
Ready To Continue
Continue to Chapter 5 when each math idea maps to a model action: represent data, compare examples, measure uncertainty, measure loss, or update parameters.