Course Information

FieldValue
Course TitleMathematics for data analytics
Course CodeM-228-04
LevelMaster
ECTS Credits6.00
Faculty/unitEngineering Faculty
FieldComputer and information sciences
Course TypeStandard
Course LeaderPavlyuk Dmitry - Dr. sc. ing. professor
AnnotationThis course will introduce and, in some cases, review mathematical concepts relevant to future work in applied data analytics. It will cover basics of linear algebra including matrix and vector algebra and eigensystems, as well as optimization methods, Bayes theorem and the concept of maximum likelihood.
AimThe aim of the course is to provide a strong background in mathematical concepts related to the data analytics field.
LO1Knows and understands fundamental properties of matrices, their norms and applications.
LO2Understands and uses multivariate functions.
LO3Knows and understands optimization methods involving matrices and multivariate functions.
LO4Understands principles of statistical estimation and knows contemporary estimation methods.
Required Literature
  • Deisenroth, M.P., Faisal, A.A. and Ong, C.S. (2020), Mathematics for Machine Learning, Cambridge University Press, Cambridge 
  • New York, NY.
  • Davis, Ernest. Linear Algebra and Probability for Computer Science Applications, CRC Press LLC, 2012. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/tsilv/detail.action?docID=1633372.
Assessment MethodThere are two assessment elements, a final examination and a set of practical assignments. The practical assignments should be completed individually by each student.
Independent studyIndependent study is organised around completing the practical assignments during the course and individual reading of the materials.
Full-time
First Sit ElementsElement Weighting, %Group WorkLinks to Results
Reports50 LO1, LO2, LO3, LO4
Presentations50 LO1, LO2, LO3, LO4

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