ELEC-E5431 - Large Scale Data Analysis D, Lecture, 7.1.2025-9.4.2025

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

Students will be able to recognize the problems of processing data of large scale that arise in engineering and computer science. The focus is in statistical inference, learning and optimization methods for large scale data.  Students will know the basic theory of such problems, concentrating on results that are useful in computation. Students will have a thorough understanding of how such problems are thought of and addressed, and some experience in solving them. Students are expected to be able to apply the methods of large scale data analysis in their own research work. They will know a number of examples of successful application of the large scale data analysis techniques in different areas. More detailed and revised learning outcomes are presented at the beginning of the course.

Credits: 5

Schedule: 07.01.2025 - 09.04.2025

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Sergiy Vorobyov, Visa Koivunen, Esa Ollila

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:

    Large and huge scale optimization, statistical learning methods of large scale data analysis, multiple hypothesis testing and false discovery rate control for large scale and distributed data, exploiting sparsity in large scale data analysis, tensor-based methods for analysis data with multiple atributes. Sequential decision making and change-point detection.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Lectures, exercises, assignments.

Workload

  • valid for whole curriculum period:

    Lectures, excercises, assignments and independent studying.

    Attendance in some contact teaching may be compulsory.

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring III
    2025-2026 Spring III - IV

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