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301169 SE Applied Machine Learning for biological problems (2019W)

Continuous assessment of course work

Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Registration is open from Th 05.09.2019 08:00 to Th 19.09.2019 18:00
Deregistration possible until Th 19.09.2019 18:00

Details

max. 20 participants

Language: English

Lecturers

Classes (iCal) - next class is marked with N

Location: Seminar room 5.43 in the (former) University of Economics building, Augasse 2-6, Core A, 5th floor

    
    Thursday
    03.10.
    15:00 - 16:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    10.10.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    17.10.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    24.10.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    31.10.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    07.11.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    14.11.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    21.11.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    28.11.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    05.12.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    12.12.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    09.01.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    16.01.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    23.01.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A
    
    Thursday
    30.01.
    15:00 - 17:00
    Seminarraum 5.43, UZA Augasse 2-6, 5.Stock  Kern A

Information

Aims, contents and method of the course

Goals: Participants will learn basic concepts of machine learning, including working environments, several specific methods, and evaluation strategies. After the course, they should be able to decide, whether a given biological problem can be tackled with machine learning. They should be able to assess the quality of machine learning-based approaches in the scientific literature. Additionally, they should also be able to solve simple biological problems with machine learning,
Content:
Data science working environments (cloud infrastructures, Python, SciPy-stack, Jupyter notebooks, etc.)
Data preprocessing
Machine learning basics (data-driven methodology, evaluation strategies, underfitting/overfitting, train-test splits, cross-validation, etc.)
Methods:
Tree-based learning (e.g. decision trees)
Kernel methods (e.g. support vector machines)
Neural networks (e.g. deep learning)
Ensembles (e.g. gradient boosting)

Assessment and permitted materials

Weekly exercises (75 pts.)
Group project/competition (25 pts.)
Bonus points are possible through additional activities (TBA)

Minimum requirements and assessment criteria

Weekly exercises must be worked on individually. An additional larger project towards the end of the course can be worked on individually or in groups.
Attendance is encouraged, since concepts required for the exercises and the project are discussed in class, and we want the seminar to be interactive.
Participants must achieve at least 50 pts. by the end of the course.
Grading:
< 50.0: 5
< 62.5: 4
< 75.0: 3
< 87.5: 2
else: 1

Students need to bring their own laptop (we can provide a few, if required).
There are no strict prerequisites, but basic command line and Python skills are recommended.

Examination topics

Everything presented in the course.
Slides are and will be available online.

Reading list

Books:
Bishop: Pattern recognition and machine learning (2006)
Hastie: The elements of statistical learning (2009)
Goodfellow: Deep learning (2016) (free online version: https://www.deeplearningbook.org/)

Papers:
Relevant scientific literature will be introduced in the course.

Association in the course directory

MMEI II-1.2, MMEI II-2.2, MMB W-2, MMB W-3, PIK 2

Last modified: Mo 05.10.2020 14:30