At the end of the course, the student:
- Will be familiar with and has practical experience with the main methods of machine learning:
- Nearest neighbors
- Bayes classifiers and discriminant analyses
- Decision trees, boosting and random forest
- Regularization methods and SVM
- Principal component analysis and partial least squares
- Neural networks and Deep learning
- Generalized linear regression
- Survival analysis
- Repeated measurements and time course analysis
- Is familiar with concepts of evaluating classifiers, such as Cross-validation and Bias-Variance tradeoff has profound knowledge of the reasons for over-fitting and complete separation with high-dimensional data is able to apply all of these methods to real data
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Education form |
Startdate |
Enddate |
Registration period |
Face-2-Face |
20-6-2022 |
24-6-2022 |
BMS_P4_A |
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Register via https://www.msc-epidemiology.nl/single-courses.html
Course coordinator:
Rene Eijkemans & Victor Jong
Course description:
Learn the basics of machine learning, with a special focus on sparse data as they occur in high dimensional ‘omics’ types of data
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
Introduction to Statistics
Classical Methods in Data Analysis
Modern Methods in Data Analysis
Prognostic Research can be useful
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