Machine Learning & Applications in Medicine

Course goals

ALL PARTICIPANTS WILL BE PLACED ON THE WAITING LIST UNTIL TWO WEEKS BEFORE THE START OF THE COURSE

GRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)

POSTGRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)  or online (depending on your registration)

(FYI: the other options are options for Continuing Education (onderwijs voor professionals))


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

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. NOTE Students of the MSc Epidemiology (Post Graduate) that register in time (i.e. at least two weeks before the start of a course) will always be admitted to the course unless it is completely full. Other students will receive information about their application two weeks before the start of the course.

Course coordinator:
Dr. B.B.L. (Bas) Penning de Vries, UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands

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 (optional):
An introduction to statistical learning, with applications in R, seventh edition. Springer. ISBN 978-1-4614-7137-0, James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert (ISL, available as pdf, see https://www.statlearning.com/) 
The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Second Edition. Springer.  ISBN 978-0-387-84858-7, Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (ESL, available as pdf, see https://hastie.su.domains/Papers/ESLII.pdf).
  
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 (preferred)
Prognostic Research can be useful