Digicampus
Projektseminar: Machine Learning in Health Care - Details
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Lehrveranstaltung wird online/digital abgehalten.

General information

Semester WS 2020/21
Current number of participants 15
expected number of participants 9
Home institute Prof. Dr. Jens Brunner - Health Care Operations / Health Information Management
Courses type Projektseminar in category Teaching
First date Tue , 06.10.2020 09:30 - 12:30
Participants The course is limited to 9 students. Therefore an application is necessary. Please register for the course on Digicampus and apply via the application form of the Chair of Prof. Brunner. From July 17 to Sept 27 the application form is available here: https://wiki.unika-t.de/Applications_for_Master_degree_courses We will inform you whether or not you can attend the course after the application period has been finished.
Pre-requisites (Advanced) Knowledge in mathematics, particularly linear algebra and stochastics; knowledge of a programming language (e.g. Python) is beneficial; interest in health care applications and team work.
Learning organization The seminar comprises six mandatory sessions with assignments to each lecture, four working sessions and an exam session.
Performance record Programming assignments, quizzes, group presentations, individual presentations, oral exam.
Online/Digitale Veranstaltung Veranstaltung wird online/digital abgehalten.
Hauptunterrichtssprache englisch
Literaturhinweise - Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer Verlag, 2006.
- Andrew Ng: Machine Learning. Stanford University. Online on Coursera: https://www.coursera.org/learn/machine-learning
- Google Developers: Machine Learning Crash Course. Online: https://developers.google.com/machine-learning/crash-course
- Prashant Natarajan, John C. Frenzel, Detlev H. Smaltz: Demystifying Big Data and Machine Learning for Healthcare. CRC Press, 2017.
- Stephen Boyd: Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares. Cambridge University Press, 2017. Online: http://vmls-book.stanford.edu/vmls.pdf
- Barry M. Wise, Neal B. Gallagher: An Introduction to Linear Algebra. Online: http://www.eigenvector.com/Docs/LinAlg.pdf
- Eric Matthes: Python Crash Course. No Starch Press, 2016.
- Official Python tutorial. Online: https://docs.python.org/3/tutorial
- Interactive Python tutorial. Online: https://www.learnpython.org/
- Other literature will be announced in the course.
Miscellanea The course is limited to 9 students. Therefore an application is necessary. Please register for the course on Digicampus and apply via the application form of the Chair of Prof. Brunner. From July 17 to Sept 27 the application form is available here: https://wiki.unika-t.de/Applications_for_Master_degree_courses We will inform you whether or not you can attend the course after the application period has been finished.
ECTS points 6

Course location / Course dates

n.a Tue , 06.10.2020 09:30 - 12:30
Fri , 09.10.2020 09:30 - 12:30
Tue , 13.10.2020 09:30 - 12:30
Fri , 16.10.2020 09:30 - 12:30
Mon , 19.10.2020 09:30 - 12:30
Tue , 20.10.2020 09:30 - 12:30
Fri , 23.10.2020 09:30 - 12:30
Tue , 27.10.2020 09:30 - 12:30
Thu , 29.10.2020 09:30 - 10:30
Fri , 30.10.2020 09:30 - 12:30
Fri , 13.11.2020 09:30 - 12:30

Module assignments

Comment/Description

Topics of the module include (but are not limited to) the following:
- Introduction to Machine Learning
- Programming in Python
- Linear regression
- Logistic regression
- Regularization
- Neural networks
- Support vector machines
- Unsupervised learning
- Insights into up-to-date research and applications