Machine Learning for Health Care
Course Details
| Number | 261-5120-00L |
| Lecturers | V. Boeva, J. Vogt, M. Kuznetsova |
| Semester | Spring 2023 |
| Language | English |
Abstract
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.
Objective
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.
Content
The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine:
1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc.
2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them.
3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them.
4) 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.
Prerequisites/Notice
Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
Additional information and lecture material can be found on the Moodle course webpage.
Location
Tue 10:15-12:00 HG D 7.2
Tue 13:15-14:00 HG D 7.2
Course Schedule
| Date | Topic | Material | Tutorial |
|---|---|---|---|
| Introduction | - | ||
| Time-Series | Introduction of Paper Presentations | ||
| Interpretability 1 | Project 1 Distribution | ||
| Imaging | Time-Series Paper Presentation | ||
| Interpretability 2 | Interpretability Paper Presentation | ||
| Representation Learning | Imaging Paper Presentation | ||
| NLP | Project 2 Distribution | ||
| Easter Break | - | ||
| - | Explainability Paper Presentation Representation Learning Paper Presentation | ||
| Genetics (Supervised) | NLP Paper Presentation | ||
| Genetics (Unsupervised) | Project 1 Presentation | ||
| Ethics | Transfer Learning Paper Presentation | ||
| Privacy | Genetics (Supervised) Paper Presentation Ethics Paper Presentation | ||
| Survival Analysis | Genetics (Unsupervised) Paper Presentation Privacy Paper Presentation | ||
| Exam Preview/Polls | Survival Analysis Paper PresentationProject 2 Presentation |