Lecture 1

import numpy as np

a = np.array( [[6,5,3,1], [3,6,2,2], [3,4,3,1] ])
b = np.array( [ [1.5 ,1], [2,2.5], [5 ,4.5] ,[16 ,17] ])

for c in (a @ b):
    print(c)

[50. 49.]
[58.5 61. ]
[43.5 43.5]

Lecture 2

Matrix decomposition: we decopose matricies into smaller ones that has special properties

Lecture 1

We learn about the big picture behind multiplication of matrix and vector

we learn about the row picture and column picture

Lecture 2

we learned about elimination method to solve a system of equations

Lecture 3

in this lecture we learned about matrices multiplication:

we can do that in five ways:

1. row * col ==> gives an entry (1 cell)
2. col _ row ==> sum ( r1 _ c1 , r2 * c2, etc)
3. by columns ==> A * c1 = combination of A columns
4. by columns ==> r1 * B = combination of A B rows
5. by blocks ==> A (A1,A2,A3,A4) _ B (B1,B2,B3,B4) = C1 = (A1_ B1 + A2 * B3) and so on

then we learned about gausian-Jordan elimination to find the matrix inverse