Inhalt

[ 536MATHAI1V19 ] VL Mathematics for AI I

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
6 ECTS B1 - Bachelor's programme 1. year (*)Artificial Intelligence Mario Ullrich 4 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2025W
Learning Outcomes
Competences
Students have a foundational understanding of the mathematical concepts and language essential to artificial intelligence, learning to interpret and manipulate mathematical expressions, symbols, and quantifiers. They possess skills to use mathematical logic, work with sets, functions, matrices, sequences, and series, and apply various proof strategies to solve problems relevant to AI.
Skills Knowledge
  • Understanding Mathematical Language and Symbols (k3)

Students can interpret and work with mathematical symbols, quantifiers, sums, and product notations, translating mathematical language into computational concepts.

  • Applying Proof Techniques (k4)

Students are able to understand and implement different proof strategies, such as direct proof, proof by contradiction, and induction, to verify mathematical statements and propositions.

  • Using Basic Logic in Mathematical Reasoning (k4)

Students can apply principles of mathematical logic, including logical operators, truth tables, and implications, to construct valid arguments and reason through mathematical problems.

  • Manipulating Sets, Numbers, and Functions (k4)

Students can perform operations on sets, understand properties of numbers, and work with functions, comprehending their roles in AI-related mathematical structures.

  • Solving Linear Equations and Working with Matrices (k4)

Students are capable of solving systems of linear equations, performing matrix operations, and applying linear algebra techniques to represent and manipulate data.

  • Analyzing Sequences and Series (k4)

Students can identify and analyze sequences and series, calculate their sums, and understand their convergence properties, which are fundamental in mathematical modeling and AI applications.

Students have foundational knowledge of the importance of mathematics in AI, gaining familiarity with mathematical notation, symbols, and logic. They understand key concepts such as sets, numbers, functions, matrices, linear equations, sequences, and series, forming a basis for deeper study in AI and advanced mathematical techniques.
Criteria for evaluation final exam
Language English
Study material Manuscript, slides, videos and exercises+solutions can be found on Moodle.
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment