Number of hours
- Lectures 9.0
- Projects -
- Tutorials 9.0
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 2.0
Goal(s)
The goal of this course it to get familiar with some basic tools of artificial intelligence and learning.
We will focus only on the linear algebra aspects of learning.
Responsible(s)
Christophe PICARD
Content(s)
The course contain 3 parts
1. Eigenvalue decomposition
a. Properties
b. Power method
c. Application : page rank
2. Singular value decomposition
a. Properties
b. General case for non square matrices
c. Application : image
3. Gradient based methods
a. Principle
b. Conjugate gradient method
Normed vector space
Test
Written examen for session 1 - N1
Written examen for session 2 - N2
N1 = E1
N2 = E2
Calendar
The course exists in the following branches:
- Curriculum - Work Study Education - Alternance 2eme annee
Additional Information
Course ID : 4MM1FMIA
Course language(s): 
The course is attached to the following structures:
You can find this course among all other courses.
Bibliography
Youssef Saad : Elementary Computational Linear Algebra
Marc Peter Deisenroth : Mathematics for Machine Learning