Lehrinhalte
[list]
[*]Introduction, terms and delimitations
[*]Data acquisition and preprocessing
[*]Feature extraction and visualization methods
[*]Statistical fundamentals
[*]Classification methods
[list]
[*]Linear Regression, Logistic Regression
[*]Support Vector Machines
[*]Decision Trees, Random Forest, XGBoost
[*]Neural Networks
[/list]
[*]Overfitting and underfitting with medical data
[*]Influence of unbalanced data sets
[*]Evaluation of algorithms
[*]"Explainable AI"
[*]Regulatory Requirements
[/list]
Literature
[list]
[*]Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
[*]Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
[/list]
Voraussetzungen (Recommended)
18-zo-1030 Fundamentals of Signal Processing
[list]
[*]Introduction, terms and delimitations
[*]Data acquisition and preprocessing
[*]Feature extraction and visualization methods
[*]Statistical fundamentals
[*]Classification methods
[list]
[*]Linear Regression, Logistic Regression
[*]Support Vector Machines
[*]Decision Trees, Random Forest, XGBoost
[*]Neural Networks
[/list]
[*]Overfitting and underfitting with medical data
[*]Influence of unbalanced data sets
[*]Evaluation of algorithms
[*]"Explainable AI"
[*]Regulatory Requirements
[/list]
Literature
[list]
[*]Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
[*]Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
[/list]
Voraussetzungen (Recommended)
18-zo-1030 Fundamentals of Signal Processing
- Lehrende: Christoph Hoog Antink
Semester: WT 2022/23