Mathematics for machine and deep learningLaajuus (5 cr)

Objective

You understand the significance of mathematics in machine learning algorithms.
You know the key concepts of linear algebra.
You understand how differential and integral calculus is applied in machine learning.
You can use probability theory and statistical methods for problem-solving.
You can solve mathematical problems using programming.

Content

What are the key topics in linear algebra for machine learning?
How are vectors and matrices operated on?
How is differentiation used in optimization?
What is the significance of the gradient?
How are probabilities used in machine learning?
What are the different types of distributions?
How is statistics used in machine learning?
How can mathematical problems be solved by programming algorithms?

Qualifications

Proficiency in calculus and programming is required.

Accomplishment methods

a. use professional vocabulary systematically.
b. look for information in the key information sources of the field.
c. identify interrelated tasks.
e. use the key models, methods, software and techniques of the professional field.
g. justify their actions according to the ethical principles of the professional field.