MS-E1622 - Algebraic Methods in Data Science, Lecture, 13.1.2023-14.4.2023

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

At the end of this course, the student can

  • describe the main algebraic methods used in data science
  • apply these methods
  • recognize problems in data science that can be solved using algebraic methods

Credits: 5

Schedule: 13.01.2023 - 14.04.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Kaie Kubjas

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    The contents of this course include the following topics:

    • Numerical algebraic geometry
    • Matrix and tensor decompositions
    • Topological data analysis
    • Graphical models

    Time permitting further topics will be considered. 

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Teaching methods: lectures and exercises

    Assessment methods: homework and final project

Workload

  • valid for whole curriculum period:

    Contact hours 36h, self-study ca 96h

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn III - IV
    2023-2024 No teaching

    Enrollment :

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