By: Richie and Jacob

1. Linear Algebra:
Applications in
Computer Science
By:
-Jacob L
-Richie

2. Common Applications of Linear Algebra
within Computer Science:
❏ Machine learning
❏ Optimization (data / code / processing)
❏ Graphing algorithms, projections and predictive models.
❏ Search engines and result order
❏ Grouping and data organization
❏ Image processing and facial recognition
❏ IP address design schemes
❏ Cryptography

3. Machine Learning
❖ Input and output:
➢ Input as vectors with various numbers to represent the
observed data, allowing for linear transformations or
various matrix operations to produce output.

4. Graphing Algorithms, Projections, and
Predictive Models.
❖ Predictive modeling and projecting given inputs:
➢ Matrices and matrix transformations allow for input data
to be projected into predictive models for various cases.
■ One particular example is Markov Chains.

5. Search Engines, Data Retrieval and
Result Order.
❖ Google’s PageRank algorithm:
➢ Developed using eigenvectors and eigenvalues to predict
useful websites based on input from previously searched
and clicked sites.

6. Image Processing and Facial
Recognition
❖ Image processing and changes such as sharpen, scaling,
shearing, blurring, and more:
➢ All of these operations can be done via multiplying and
manipulating matrices with values assigned to pixels.
➢ Also used for facial recognition software to determine
facial features.

7. Cryptography
❖ Matrix Transformations can be used for encryptions
➢ The product of an encryption matrix and a ciphertext
matrix
❖ Hill ciphers use linear systems
➢ Decryption through solving