261-5120-00L Machine Learning for Health Care (Spring 2021)


The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems.


During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions.


The course will consist of several topic clusters that will cover the most relevant applications of ML in Biomedicine:

  1. Analysis of medical imagesImages are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them.
  2. Analysis of genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges.
  3. Analysis of text and representation learning: Vast amount of medical observations are stored in the form of free text, we will analyze strategies for extracting knowledge from them.
  4. Analysis of time series and sequence data: Temporal time series or sequential data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc.
  5. Interpretability & Privacy of ML methods. We will discuss the need for interpretable ML models, and we will discuss how differential private data can be generated e.g. by using GANs.


Prerequisites / Notice

Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line.

It is helpful but not essential to attend Computational Biomedicine before attending Machine Learning for Health Care.


The lecture will be held via Zoom.

Course Overview

Date Topic Course Materials
23.02.2021 -- No Lecture --
02.03.2021 Introduction Lecture Slides 01
Tutorial Slides 01
09.03.2021 Medical Imaging Analysis Lecture Slides 02
Tutorial Slides 02
16.03.2021 Supervised methods for Genetics and Transcriptomics Lecture Slides 03
Paper presentation 1, Paper presentation 2
23.03.2021 Unsupervised methods for Genetics and Transcriptomics Lecture Slides 04
Tutorial Slides 04
30.03.2021 Ethics Lecture Slides 05
Paper presentation 1
06.04.2021 -- Easter Break --
13.04.2021 -- No Lecture -- Paper presentation 1
20.04.2021 Survival Analysis Lecture Slides 06
Paper presentation 1, Paper presentation 2, Paper presentation 3
27.04.2021 Natural Language Processing Lecture Slides 07
Tutorial Slides 07
04.05.2021 Representation Learning Lecture Slides 08
11.05.2021 Time-Series and Sequence Analysis Lecture Slides 09
Tutorial Slides 09
18.05.2021 Interpretability Lecture Slides 10
25.05.2021 Privacy Lecture Slides 11
Tutorial Slides 11
10.06.2021 Exam/Polls/Feedbacks/etc Lecture Slides 12


Project Topic Description Data Deadline
Medical imaging slides link Download here 22.03.2021
Genetics slides link Download here 26.04.2021
NLP on medical text slides link Download here 17.05.2021
Medical time series slides link Dataset 1
Dataset 2