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AI & Machine Learning Worksheet
🤖 Linear Algebra in NumPy
SCORE___ / 20
NameClassDate
After this worksheet you can
Create arraysDot and matmulTranspose and inverse in codeVectorised speed📚 Quick Recap
Lessons 2.1 through 2.4 covered vectors, matrices, dot products, norms, multiplication, and eigenvalues on paper. This lab puts every one of those ideas into actual running Python code using NumPy — the library that every other tool in this course track (pandas, scikit-learn, PyTorch) is ultimately built on top of.
🧠 Section A · Concept Check ● BEGINNER 4 × 1 = 4
1Dot product in NumPy:
2Matrix multiply operator:
3Transpose of array A:
4Inverse comes from:
🧮 Section B · Problem Solving ● INTERMEDIATE 2 + 3×3 = 11
5np.array([1,2,3]).shape is (,).
6np.zeros((2,3)) makes a 2x3 of .
7Code: dot of [1,2] and [3,4].
8Code: 2x2 identity times [[1,2],[3,4]].
9Why is NumPy 100x faster than loops?
🚀 Section C · Challenge ● CHALLENGE 5
10Check a matrix times its inverse.
💭 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 3 6 zeros 7 = np.dot([1,2],[3,4]) -> 11 8 = np.eye(2) @ A -> A 9 = C-optimised vectorised operations on whole arrays | 10 = A @ np.linalg.inv(A) is approx np.eye(n)