Content of Learning Activity

Introduction to Artificial Intelligence: In this section, students will be introduced to the fundamental concepts of artificial intelligence (AI), its history, evolution, and growing importance in healthcare. They will also learn to distinguish between AI, machine learning and deep learning.
Supervised learning: Supervised learning will be presented as one of the main approaches to machine learning. Students will learn basic concepts such as training and testing data, labels, and features. They will also learn about common algorithms such as linear regression, logistic regression, support vector machines, and decision trees, and their applications in the healthcare industry.
Unsupervised learning: This section will focus on unsupervised learning, another key approach to machine learning. Students will learn how unsupervised algorithms work without data labels and discover popular techniques such as hierarchical classification, K-means clustering, self-organizing maps, and principal component analysis. They will also explore how these methods can be applied to solve health problems.
Deep learning: Deep learning, a subcategory of machine learning based on artificial neural networks, will be presented in this section. Students will become familiar with key concepts such as neural layers, activation functions, and backpropagation. They will also investigate popular neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and their relevance for specific healthcare tasks, such as medical image recognition and analysis of genomic sequences.
Uncertain Reasoning: In this final section, students will learn how to manage the uncertainty and imprecision inherent in medical data using uncertain reasoning techniques. They will be introduced to approaches such as probability theory, fuzzy set theory, Bayesian networks and Markov chains. Students will discover how these techniques can be applied to make informed decisions and model complex processes in healthcare
 

Required Learning Resources/Tools

Not applicable
 

Recommended Learning Resources/Tools

not applicable
 

Other Recommended Reading

Not applicable
 

Mode of delivery

• Hybrid

Type of Teaching Activity/Activities

• Cours magistraux

Evaluations

The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)