Lecture 1

1

import numpy as np

1
2

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] ])

1
2

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

Singular Value Decomposition (SVD):

• it’s an exact decomposition, so you can retrieve the orginal matrix again

Some SVD applications:

• semantic analysis
• collaborative filtering / recommendation
• data compression
• PCA (principal component analysis)

Non-negative Matrix Factorization (NMF)