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Course module: BMB518818
BMB518818
Machine Learning & Application in Medicine
Course info
Course codeBMB518818
EC1.5
Course goals
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
Content
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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Kies de Nederlandse taal