Course Applied Machine Learning in health Sciences

ECTS: 3.5

Course leader: Peter Mondrup Rasmussen

Language: English

Graduate school: Faculty of Health

Graduate program: ClinFO

Course fee: 4,200.00 DKK

Status: Course is open for application

Semester: Spring 2024

Application deadline: 04/02/2024

Cancellation deadline: 18/02/2024

Course type: Classroom teaching

Start date: 04/03/2024

Administrator: Thilde Møller Risgaard

The course C308/03 Applied machine learning in health sciences is being offered by the Graduate School of Health, Aarhus University, spring 2024.

Criteria for participation:
University degree in medicine, dentistry, nursing, or master’s degree in other fields and/or postgraduate research fellows (PhD students and research-year medical students).

Requirements for participation:

  • Experience with programming is required, e.g. variable types (cells, structs, tables, strings), functions, loops (if, while, for), scripts, basic plotting and visualization, import/export data. For example, AU PhD course C171/11 Introduction to data analysis for health sciences using MATLAB.
  • Programming code used in lecture examples and in exercises will be provided as both Matlab, R, and Python code. Students are thereby welcome to work in their favorite programming language.
  • Knowledge on basic statistics, e.g. linear modeling/regression, ANOVA, and biostatistics courses will be an advantage.

The aim of the course is to introduce the student to machine learning techniques and enable the student to apply these methods to analyze complex data sets as typically encountered in modern research. The student will achieve an understanding of the theoretical background of supervised- and unsupervised machine learning techniques and will gain practical experience in applying these techniques in real-world data analysis.

Learning outcomes:
A student who has met the objectives of the course will be able to:

  • Describe main steps involved in typical machine learning analyses, including data preparation, data modeling, model evaluation, and result dissemination.
  • Describe the mathematical and statistical principles in supervised- and unsupervised machine learning.
  • Describe basic and advanced methods for predicting continuous- and discrete outcomes (regression and classification).
  • Describe procedures for model building, model selection and model evaluation.
  • Identify relevant machine learning techniques to solve research-based problems.
  • Design and implement a solution strategy to solve research-based problems.
  • Apply unsupervised- and supervised machine learning techniques to their own data.
  • Disseminate the analysis result and account for the solution strategy and analysis results as necessary for publication in scientific journals.

Workload: The full workload of the course is expected to be 78.5 hours


Technical content:

  • Data preprocessing, feature extraction and feature representation. Unsupervised machine learning techniques including techniques for dimension reduction (e.g. principal component analysis) and techniques for clustering (e.g. k-means, hierarchical clustering). Supervised machine learning techniques including techniques for modelling continuous outputs (regression) and discrete outputs (classification) (e.g. linear regression, logistic regression, support vector machines, neural networks). Techniques for complexity control (e.g. feature selection, shrinkage methods), and techniques for model selection and model evaluation (e.g. cross-validation).

Course structure:

  • Guided self-study (textbook, notes, video-clips). The student will gain knowledge on the conceptual- and theoretical basis of the modeling- and machine learning techniques.
  • In-class activities (five days 8-16) with mixture between short lectures, hands-on exercises, and group work. Key concepts will be highlighted in lectures, but the focus is on student-oriented learning with conceptual/theoretical exercises and practical data analysis (computer).
  • Students should expect to set aside working hours outside the classroom to complete in-class exercises between course days.
  • Data will be from the health science research domain.

Course material:

  • Textbook: Introduction to Statistical Learning. James, Witten, Hastie, Tibshirani.
  • Video-lectures
  • Notes and exercise material

Course evaluation:

  • As the course progresses, the student hands in in-class exercises. Two weeks after the last course day, the student hands in i) completed exercise portfolio based on in-class exercise work, ii) group report. Pass/fail, internal evaluation.

Peter Mondrup Rasmussen.

Venue: Aarhus University, Aarhus

Participation in the course is without cost for:

Course dates:

  • 04 March 2024 08:00 - 16:00
  • 06 March 2024 08:00 - 16:00
  • 08 March 2024 08:00 - 16:00
  • 11 March 2024 08:00 - 16:00
  • 13 March 2024 08:00 - 16:00