Objectives and competences
Students enhance their knowledge of fundamental mathematical tools in data science. They become proficient in applying theoretical knowledge from the fields of linear algebra and mathematical analysis, as well as using the MATLAB program to solve problems and study models and algorithms. They conduct critical evaluation based on solid theoretical foundations.
Content (Syllabus outline)
1. Matrices, basics notions
2. Invertible matrices and generalized matrix inverses
3. Eigenvalues and eigenvectors, matrix decompositions and matrix diagonalization
4. Vector spaces, linear mappings, and matrix representation of linear mappings
5. Partial derivatives of functions of several variables and multivariate chain rule, maxima, minama, and Lagrange multipliers, gradients of vector-valued functions, higher order derivatives, linearization and multivariate Taylor series, gradient descent
6. Applications
Learning and teaching methods
- written examination
- active participation in lectures and laboratory work
Student may replace the written examination with two written tests which are equivalent to the written examination.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding
Students:
- Gain systematic knowledge of mathematical foundations of data science.
- Can identify and use adequate analytical concepts and tools.
- Are able to analyze complex problems and search for the most effective solutions.
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills
- with critical awareness, can undertake analysis
- managing complexity and incompleteness of data or potential contradictions which may appear in practice
- can act independently in problem solving.
Key/Transferable skills
- ability of additional learning and individual study of new methods in the field of data science
- capacity to adapt to new situations
- interest in lifelong learning
- ethical commitment
Practical skills:
- capability of understanding and application of knowledge in praxis
- capability to use a modern software tool in problem solving
Readings
Obvezna študijska literatura (Compulsory textbook):
Deisenroth, M. P., Faisal, A. A., Ong, C. S. (2020). Mathematics for machine learning, Cambridge University Press, Cambridge, United Kingdom. Pridobljeno 26. aprila 2023: https://mml-book.github.io/book/mml-book.pdf.
Dodatna študijska literatura (Additonal textbook):
Aggarwal, C. C. (2020). Linear algebra and optimization for machine learning, Springer Nature Switzerland, Cham, Switzerland. Pridobljeno 26. aprila 2023: https://link.springer.com/book/10.1007/978-3-030-40344-7.
