Course Information

Weekly Schedule

The course is held on the second term. The schedule for A.A. 2024/25 is provided in table below.

The first lecture of the course will be ON FEBRUARY 18th 2025 h. 16.00. The course will be in person, with lecture videos being recorded and made available to course students (with no guarantee of quality nor completeness).

Course Prerequisites

Course prerequisites include knowledge of elements of probability and statistics, calculus and optimization. Previous programming experience with Python is a plus for the practical lectures.

Course Overview

This course provides a comprehensive foundation in machine learning (ML) and deep learning (DL), focusing on their practical applications in healthcare. Students will gain proficiency in key programming libraries and explore how AI methodologies can be leveraged for patient and risk stratification, disease prediction, and modeling disease progression.

Special attention is given to the unique challenges of working with health data, including physiological time-series, clinical text, and medical imaging. Through hands-on lab sessions, students will implement and analyze models for supervised prediction, clinical text processing, interpretability analysis, and causal inference in healthcare contexts. This course equips students with the essential skills and insights needed to harness ML/DL technologies for transformative healthcare solutions.

Topics: Fundamentals of Probability and Statistics for AI, Fundamentals of Machine Learning, ML for Risk Stratification and Diagnosis, Bayesian Networks in Healthcare, Deep Learning Fundamentals, Medical Imaging Data, Deep Learning for Medical Imaging, Sequential Data in Health, ML with Clinical Text: Natural Language Processing, Attention and Transformer Models, Language Models for Clinical Text and Medical Imaging, Graphs in Health and Life Sciences, Deep Learning for Graphs in Health, Tackling Challenges in Healthcare Data Processing, and Deployment of AI-based Applications for Health

Textbooks and Teaching Materials

Much of the course content will be available through lecture slides and associated bibliographic references. 

We will use two main textbooks, one covering more general fundamentals of artificial intelligence and machine learning, and the other more oriented towards applications of AI and statistics to healthcare.

Note that all books have an electronic version freely available online.

[SD] Simon J.D. Prince, Understanding Deep Learning, MIT Press (2023) (book online and additional materials)

[AI4H] G.J. Simon, C. Aliferis, AI and ML in health Care and Medical Sciences, Springer, 2024 (open book online)

Useful Jupyter Notebooks

Familiarity with Python libraries such as NumPy, Pandas, and MatPlotLib is assumed. In case you need a knowledge refresher, here are some comprehensive notebooks:

• Introduction to NumPy
• Another introduction to NumPy
• Introduction to Pandas
• Another (shorter) introduction to Pandas
• Yet another introduction to Pandas
• The numpy.random module
• Introduction to MatPlotLib