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301169 SE Applied Machine Learning for biological problems (2020W)

5.00 ECTS (3.00 SWS), SPL 30 - Biologie
Continuous assessment of course work

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).

Details

max. 20 participants
Language: English

Lecturers

Classes

Dates Nov 16-27 (every day):
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Lectures (compulsory attendance)
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Nov 16 09:00–10:00
Nov 17 09:00–10:00
Nov 18 09:00–10:00
Nov 19 09:00–10:00
Nov 20 09:00–10:00
Nov 23 09:00–10:00
Nov 24 09:00–10:00
Nov 25 09:00–10:00
Nov 26 09:00–10:00
Nov 27 09:00–10:00
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Additional daily exercises
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Location: Seminar room 5.43 in the (former) University of Economics building, Augasse 2-6, Core A, 5th floor
To be held in mixed mode (presence/online).


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

Ten daily exercises (100 pts. each)

Additional activities for bonus points may be introduced during the course depending on progress and specific interests (TBA)

Minimum requirements and assessment criteria

A standard PC/laptop is required (any OS, Linux preferred). We can provide a few laptops, if need be.
For online attendance, a Zoom account is required.
Options for high performance computing (cluster or cloud services) will be provided in the course.

There are no strict course prerequisites, but basic command line and Python skills are recommended.
You will write Python snippets and small programs in each exercise.
While the seminar will start with an introduction to Python, some basic knowledge beforehand is beneficial.
Online tutorials for Bash and Python will be provided at the start of semester.
Beginners are encouraged to self study before the course starts.

Daily exercises must be worked on individually. There may be an additional larger project towards the end of the course which 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.
Physical presence in the seminar room will be limited to 11-13 students max, subject to change due to COVID-19 regulations.

Participants must achieve at least 500 pts. by the end of the course.
Grading:
< 500: 5
< 625: 4
< 750: 3
< 875: 2
else: 1

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 07.09.2020 15:09