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Machine Learning & Applications in Medicine

Content partner
Julius Center
Course coordinator
Dr. S. (Said) El Bouhaddani

Machine Learning & Applications in Medicine
17 - 21 June, 2024
Course co-ordinator

Dr. Ir. Said El Bouhaddani

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: The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Second Edition.Springer.  ISBN 978-0-387-84858-7 Authors: Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome
Course objectives

At the end of the course, the student:

Will be familiar with and has practical experience with the main methods of machine learning:

o    Nearest neighbors

o    Bayes classifiers and discriminant analyses

o    Decision trees, boosting and random forest

o    Regularization methods and SVM

o    Principal component analysis and partial least squares

o    Neural networks and Deep learning

o    Generalized linear regression

o    Survival analysis

o    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.

Prerequisite knowledgeIntroduction to Statistics, Classical Methods- and Modern Methods in Data Analysis
Prognostic Research can be useful 
Course days 
Monday, Tuesday, Wednesday, Thursday, Friday
Course format
Lectures, computer practicals, group presentations, group exercises
Daily quizzes (individual) and the analysis of a case study that is presented to classmates on the last afternoon.

All elements have to be awarded with at least a 5.5 in order to pass the final Assessment.

Number of participants
Course fee
€ 980,-


Maximum participants
Fee (€)

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