CS-E4885 - Machine Learning in Biomedicine D, Lecture, 6.9.2024-29.11.2024

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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

The students will learn how machine learning used in different biomedical applications.
Students will get training on scientific work, presenting research and giving feedback on other student's work.

Credits: 5

Schedule: 06.09.2024 - 29.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Harri Lähdesmäki, Juho Rousu, Vikas Garg, Pekka Marttinen

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:

    Machine learning is one of the key technologies in data-driven biomedicine, used in numerous tools and applications. This course probes the state of the art in selected machine learning problems and the associated methods in biomedicine, through introductory lectures and student's own work in selected topics.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    To be specified in MyCourses at the start of the course.

Workload

  • valid for whole curriculum period:

    The course workload consists mostly of independent work (115 hours) and small amount of contact teaching (12 hours).

    Details will be specified in MyCourses at the start of the course.

    Course cannot be completed remotely.

DETAILS

Study Material

  • valid for whole curriculum period:

    To be specified in MyCourses at the start of the course.

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

    Registration:

    Registration to the course is limited. The following criteria will be used to select students:

    • Students Majoring in Bioinformatics and Digital Health will have priority
    • Amount and study success of relevant background courses (see course prerequisites)

     

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