Topic outline

  • General

    Overview:

    This course introduces statistical and machine learning methods for analyzing various types of biomedical data in different health related applications. Typical analysis challenges include e.g. longitudinal data analysis, biomarker identification, survival analysis, natural language processing for textual healthcare data analytics, genetic association analysis and drug response modeling. During the lectures you will learn about a collection of biomedical problems together with standard and state-of-the-art statistical and machine learning methods to address those problems and to make biological/health conclusions at the end. 

    Prerequisite knowledge:

    Prerequisite knowledge for this course include basic bioinformatics and basic statistics/machine learning, e.g. CS-E5860 - Computational Genomics, CS-E5870 - High-Throughput Bioinformatics, NBE-E4030 - Experimental and Statistical Methods in Biological Sciences, CS-E3210 - Machine Learning: Basic Principles, or equivalent knowledge. 

    Organisation of the course:

    The course will be organised as a hybrid course that consists of lectures both on campus and via zoom. We'll try to make the on campus lectures available also via zoom. 

    Lectures are on Fridays, 12:15-14:00. 

    Requirements:

    To pass the course you need to: 

    • Attend all lectures (1 absence allowed, further absences can be compensated by completing extra reviewing duties). 
    • Learning diaries from all lectures. 
    • A literature review report: a survey summarizing existing scientific papers on a chosen topic. Own research/experiments are not necessary but are allowed.
    • Reviewing duty: review other student's literature report and provide constructive feedback

    General information: 

    • Credits: 5 ECTS
    • Teachers in charge: Harri Lähdesmäki
    • Lecturers: Miika Koskinen, Juho Timonen, Siddharth Ramchandran, Mine Ögretir, Hans Moen, Yogesh Kumar, ...

    Tentative schedule:

    04.03: Lecture 1: Story of clinical data, Miika Koskinen, Helsinki University Hospital, University of Helsinki (online via zoom, see Lectures page for the zoom link)

    11.03: Lecture 2: Survival analysis and event risk prediction from biomarkers, Mine Ögretir, CS/Aalto (online via zoom)

    18.03: Lecture 3: Gaussian process modeling of longitudinal biomedical data, Juho Timonen, CS/Aalto, lecture on campus/T3 (we will also try to arrange an online zoom connection)

    25.03: Lecture 4: Deep generative modelling for biomedical data, Siddharth Ramchandran, CS/Aalto (online via zoom)

    01.04: Lecture 5: Deep sequential models for Electronic Health Records, Yogesh Kumar, CS/Aalto (online via zoom)

    08.04: Lecture 6: Towards automated structuring of nursing text, Hans Moen, CS/Aalto (online via zoom)

    22.04: Lecture 7: Cancelled

    13.05: Literature review report deadline

    20.05: Review feedback deadline

    25.05: Deadline for the final literature report