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301169 SE Applied Machine Learning for biological problems (2019W)
Continuous assessment of course work
Labels
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
Assessment and permitted materials
Weekly exercises (75 pts.)
Group project/competition (25 pts.)
Bonus points are possible through additional activities (TBA)
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: 1Students 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.
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: 1Students 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.
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.
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
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)