|Course name||Project seminar: Machine Learning in Health Care|
|Current number of participants||12|
|expected number of participants||9|
|Home institute||Prof. Dr. Jens Brunner - Health Care Operations / Health Information Management|
|Courses type||Project seminar in category Teaching|
|First date||Tue., 24.05.2022 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 email@example.com 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 organisation||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.|
- 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 firstname.lastname@example.org 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.|