Curricula

Objective

The course gives you a strong foundation in the mathematical concepts that are essential for the application of machine learning algorithms and methods. You master the basics of probability and the concept of randomness. You master the key concepts and methods of linear algebra, statistics and combinatorics. You can use and apply libraries to solve mathematical problems.

EUR-ACE Knowledge and understanding
You have knowledge and understanding of the principles of machine learning algorithms and methods. You understand the importance of probability and randomness in machine learning

EUR-ACE Engineering practice
You can apply probability calculation and randomness to machine learning methods in practice

Content

In this course, you will gain a strong foundation in mathematical concepts essential for applying machine learning algorithms and methods. You will learn the basics of probability calculus and the concept of randomness, as well as master key concepts and methods in linear algebra, statistics, and combinatorics. By the end of the course, you will be able to use and apply libraries to solve mathematical problems. This course equips you with the skills to apply probability calculus and randomness to machine learning methods in practice.

Probability
Linear algebra
Statistics
Combinatorics
Numerical computation

Qualifications

Basics of programming

Assessment criteria, satisfactory (1)

Sufficient (1)
You are partially familiar with the mathematical foundations of machine learning.
You know the basic concepts of probability.
You know the basics of linear algebra and statistics.
You can use the NumPy library.

Satisfactory (2)
You know the mathematical basics of machine learning.
You know the basics of probability and the concept of randomness.
You know some of the most central concepts and methods of linear algebra and statistics.
You can use the NumPy library to solve some mathematical problems.

Assessment criteria, good (3)

Good (3)
You know the mathematical basics of machine learning.
You know the basics of probability and the concept of randomness.
You know the concepts and methods of linear algebra and statistics.
You can use the NumPy library to solve well-defined mathematical problems.

Very good (4)
You have a good understanding of the mathematical principles of machine learning and can analyze the obtained results.
You master the basics of probability and the concept of randomness.
You know the key concepts and methods of linear algebra, statistics and combinatorics.
You can apply the NumPy library to solve mathematical problems.

Assessment criteria, excellent (5)

Excellent (5)
You can apply the mathematical principles of machine learning and analyze and interpret the results obtained.
You have an excellent command of the basics of probability and the concept of randomness.
You master the key concepts and methods of linear algebra, statistics and combinatorics.
You are able to apply the NumPy library to a wide range of mathematical problems, and understand the differences between the various approaches.