Digicampus
Projektseminar: Machine Learning in Health Care - Details
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Lehrveranstaltung wird als Hybrid/gemischt abgehalten.

General information

Semester SS 2022
Current number of participants 13
expected number of participants 9
Home institute Prof. Dr. Jens Brunner - Health Care Operations / Health Information Management
Courses type Projektseminar in category Teaching
Next date Thu , 30.06.2022 15:45 - 19:00, Room: (CIP 2114)
Participants The course is limited to 12 students. Therefore an application is necessary. Please register for the course on Digicampus and apply via an e-mail to alexander.horn@uni-a.de with the forms attached in the respective folder, your CV and your transcript till April 27, 2022. 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, three working sessions and an oral exam session.
Performance record Programming assignments, quizzes, group presentations, individual presentations, oral exam.
Online/Digitale Veranstaltung Veranstaltung wird als Hybrid/gemischt 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 12 students. Therefore an application is necessary. Please register for the course on Digicampus and apply via an e-mail to alexander.horn@uni-a.de with the forms attached in the respective folder, your CV and your transcript till April 27, 2022. 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

(CIP 2114) Tuesday: 15:45 - 19:00, weekly (6x)
Thursday: 15:45 - 19:00, weekly (5x)
(zoom) Tuesday. 12.07.22 15:45 - 19:00

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