Topic outline

  • General

    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.

    Teachers

    Juho Rousu (juho.rousu@aalto.fi) and Vikas Garg (vikas.garg@aalto.fi).

    Course position and prerequisites

    The course is targeted to second year MSc students and PhD students with background in machine learning.

    Compulsory prerequisites: 

    1. At least one of the following courses, or equivalent knowledge : 

    • Supervised machine learning or Machine learning: supervised methods 
    • Deep learning 
    • Probabilistic machine learning or Machine learning: Advanced probabilistic models 

    2. Programming skills

    Recommended prerequisites: 

    Studies in bioinformatics (e.g. courses High throughput bioinformatics or Modelling biological networks)

    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 

    We will notify students of the acceptance to the course after the registration deadline 30.8.

    Time and Place

    Fridays 12:15-14:00, 6.9.2024-29.11.2024, lecture room T3, Aalto CS Building

    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.

    Completing the course

    The course is completed through the following components:

    • Attending the lectures (compulsory, 1 absence allowed). Absense from first lecture is not allowed!
    • Project work (in groups of ca. 3-4 students)
    • Poster presentation (in groups)
    • Oral presentation (in groups)
    • Learning diaries of guest lectures (individually)
    • Final report (in groups)
    Learning diary

    Everyone writes independently a learning dairy entry from the lectures 13.9. onwards. At the end of course, a separate summarizing learning diary entry is to be written.

    Guideline how to write a learning diary is given in the Guidelines tab.

    The learning diary entries are submitted in the return boxes in the assignments tab. 

    Note that note learning diary entries need to be written on the scientific methods tutorials given by Juho. 

    Oral presentations

    The groups choose a scientific paper that presents a new method (algorithm, model, etc.) for an oral presentation. The survey papers listed in the literature tab are a good starting point for the search, but should not themselves be chosen for the oral presentation. The oral presentations should have length 15 minutes + 5 minutes for questions.

    Projects

    The groups choose their project topic based on their interest. The topic of the paper the group chooses for the oral presentation is a good starting point but the group may change the topic from the oral presentation as well.

    The the projects may be for example of the following types:

    • Demo : the group implement a method described in the literature and rigorously evaluate it
    • Comparison :  i.e. comparing existing implementations on selected dataset
    • A mixture of the above

    The group describes the project topic using the project topic abstract.

    Poster

    The group presents the results of their project to the other groups in a poster session.  The poster should follow the Aalto poster style (template available)

    Final report

    Written in Bioinformatics journal (Oxford university press) style. Please use the templates offered by the journal. Length of the report should be 6-10 pages Writing in LateX is recommended. 

    Schedules

    First lecture: Friday 6.9. at 12:15. Note: attending to first lecture is compulsory, since where forming the groups for the groupwork!

    Tentative schedule:

    Period I

    September 6:  

    Introduction, Organization in groups, 

    Tutorial by Juho Rousu: How to find scientific information

    September 13: 

    Lecture: by Vikas Garg
    Q/A session for groupwork

    September 20: 

    Guest lecture by Tapio Pahikkala

    Tutorial by Juho Rousu: How to present a paper

    September 27:  

    Guest lecture by Tianduanyi Wang

    Q/A session for groupwork

    October 4: 

    Oral presentations by students

    October 11: 

    Guest lecture by Heli Julkunen

    Q/A session for groupwork

    Project topic proposal deadline

    Period II

    October 25: 

    Guest lecture by Ville Mustonen,

    Tutorial by Juho: How to write a paper

    November 1: 

    Guest lecture by Tero Aittokallio,  

    Tutorial by Juho: How to present a poster

    November 8:  

    Guest Lecture by Markus Heinonen

    Q/A session for groupwork

    November 15: 

    Guest Lecture by Julius Sipilä, Orion

    Draft report submission deadline. 

    November 22: 

    No lecture

    Feedback of the draft report; Q/A session 

    November 29: 

    Poster session

    December 15: 

    Final report submission deadline