BitWithBite
AI & Machine Learning Worksheet
🤖 Scalars, Vectors & Matrices
SCORE___ / 20
NameClassDate
After this worksheet you can
Distinguish scalar/vector/matrixShapes and dimensionsData as matricesIndex elements📚 Quick Recap
Here's the honest motivation before any notation: every piece of data a machine learning model touches gets turned into numbers, and those numbers get organized into vectors and matrices so the computer can do millions of calculations on them at once instead of one at a time. An image is a matrix of pixel brightness values. A sentence becomes a list of numbers (you'll see exactly how in Tier 4). A dataset of houses with their size, location, and price is a matrix where each row is one house.
🧠 Section A · Concept Check ● BEGINNER 4 × 1 = 4
1A single number is a:
2[3, 1, 4] is a:
3A table of numbers rows x cols is a:
4A dataset of 100 samples x 5 features is shape:
🧮 Section B · Problem Solving ● INTERMEDIATE 2 + 3×3 = 11
5A vector has dimension (a list).
6A matrix has rows and .
7Give the shape of a 3x2 matrix and write one.
8Why is data stored as matrices for ML?
9Scalar vs vector vs matrix in one line each?
🚀 Section C · Challenge ● CHALLENGE 5
10What shape is a 28x28 grayscale image?
💭 Reflection — the most useful thing I learned:
A ___/4 B ___/11 C ___/5 Total ___/20
Teacher's Signature
Parent's Signature
✂ answer key — fold or cut before handing out
1-A 2-A 3-A 4-A | 5 one 6 columns 7 = (3,2): [[1,2],[3,4],[5,6]] 8 = Vectorised math on whole datasets is fast (GPU/BLAS) 9 = Number; ordered list; grid of numbers | 10 = (28,28) matrix - flattened: 784-vector