Objectives of Programme's Learning Outcomes

• Understand, describe, analyse and prioritise biological phenomena
• Understand basic scientific knowledge and be able to use it
• Problem-solve
• Understand the expression of biological realities in absolute or relative terms, the orders of magnitude, proportions and probability
• Abstract, understand and apply mathematical translations of the main laws and biological phenomena
• Understand and use different graphical representations of numerical values and their relationships
• Perceive spatial distribution, and understand two- and three-dimensional representations and interconvert them
• Understand the chronology of a phenomenon and master the time scales and their representations
• Manipulate concepts of concentration and prepare solutions
• Control the molecular, morphological and functional approaches of normal and pathological conditions
• Understand the learning of physiological and pharmacological reasoning
• Understand experimental protocols in the biomedical domain
• Integrate concepts from different approaches/disciplines in a complex biomedical problem
• Explain how molecular, morphological and functional modifications constitute pathological states and vice versa
• Develop reasoning skills
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, the rule of three, syllogism, analogy, Boolean logic, etc.)
• Understand the statistical and/or epidemiological methods
• Work with efficiency / accuracy / precision
• Present a hypothesis and hypothetical-deductive reasoning
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Manage doubt and uncertainty
• Demonstrate developed interpersonal skills
• Use a rich vocabulary linking concepts and words accurately
• Adapt lexical and syntactic choices in communication register (everyday language, medical language or scientific terminology)
• Summarise, explain, and argue
• Work in a team
• Share knowledge and information
• Submit reviews, reports and give oral presentations
• Manage resources
• Manage time
• Prioritise
• Use basic IT and bibliographic resources.
• Manage their studies
• Locate scientific information efficiently
• Compare different sources of information
• Read, interpret, and critique a scientific article
• Self-assess and give feedback
• Be open to research and demonstrate scientific curiosity
• Show a good knowledge of scientific English
• Understand and summarise a scientific article in English

Learning Outcomes of UE

Upon completion of this module, students will be able to:

Understand and differentiate the key concepts of artificial intelligence (AI), machine learning and deep learning.
Move from statistical reasoning to AI reasoning by understanding the fundamentals of machine learning algorithms.
Identify the main types of machine learning (supervised, unsupervised, and reinforcement) and their applications in healthcare.
Analyze and choose appropriate algorithms to solve specific health problems based on the available data and the objectives of the analysis.
Gain a basic understanding of data preprocessing, feature selection, and dimensionality reduction techniques.
Evaluate and interpret the performance of machine learning models using appropriate metrics, such as precision, recall, F1-score, and ROC curve.
Demonstrate an understanding of the ethical and regulatory issues related to the use of AI in healthcare.

UE Content: description and pedagogical relevance

Module content:
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.

Prior Experience

None