Mathematics for Artificial Intelligence I
15.0 ECTS creditsThe course comprises three modules and covers areas of mathematics relevant to artificial intelligence.
Module 1: Discrete Mathematics and Logic (2.5 ECTS cr)
This module covers combinatorics, sets and relations, propositional logic, real and complex numbers. Key concepts include permutations, combinations, propositions, implication, equivalence, intersection, union, relation, and the concept of numbers.
Module 2: Analysis (5 ECTS cr)
This module covers function theory and differential and integral calculus in one and several dimensions. Key concepts include functions, injective and surjective functions, limits and continuity, derivatives, and integrals. Relevant methods include differentiation and integration techniques.
Module 3: Linear Algebra with Computations (7.5 ECTS cr)
This module covers the following key concepts: vectors and Euclidean vector spaces, linear transformations, matrices, systems of linear equations, determinants, eigenvalues, and eigenvectors. Relevant methods include Gaussian elimination, matrix factorisations, determinant and eigenvalue calculations.
Module 1: Discrete Mathematics and Logic (2.5 ECTS cr)
This module covers combinatorics, sets and relations, propositional logic, real and complex numbers. Key concepts include permutations, combinations, propositions, implication, equivalence, intersection, union, relation, and the concept of numbers.
Module 2: Analysis (5 ECTS cr)
This module covers function theory and differential and integral calculus in one and several dimensions. Key concepts include functions, injective and surjective functions, limits and continuity, derivatives, and integrals. Relevant methods include differentiation and integration techniques.
Module 3: Linear Algebra with Computations (7.5 ECTS cr)
This module covers the following key concepts: vectors and Euclidean vector spaces, linear transformations, matrices, systems of linear equations, determinants, eigenvalues, and eigenvectors. Relevant methods include Gaussian elimination, matrix factorisations, determinant and eigenvalue calculations.
Progressive specialisation:
G1N (has only upper‐secondary level entry requirements)
Education level:
Undergraduate level
Admission requirements
General admission requirements plus upper secondary level Mathematics 3c/D
Selection:
Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.
This course is included in the following programme
- Artificial Intelligence - Bachelor Programme in Computer Science (studied during year 1)