Knowledge of linear algebra, mathematics, statistics, programming, signal processing, and medical imaging is recommended.
Students acquire the knowledge and develop the skills listed below:
1. Understand the fundamentals of AI and machine learning applied to biomedical data analysis and clinical process improvement.
2. Develop and evaluate AI models to solve specific problems in the healthcare field.
3. Apply programming tools to implement AI algorithms, using environments and libraries such as Python, TensorFlow, Keras, or Scikit-Learn.
4. Identify and address ethical and regulatory challenges related to the use of AI in healthcare settings, ensuring data privacy and security.
5. Encourage interdisciplinary work between engineering professionals and the healthcare sector, promoting innovative and evidence-based solutions.
1. Introduction to Artificial Intelligence (AI)
- Definition and applications of AI, history, evolution, and types. Differences between AI, Machine Learning (ML), and Deep Learning.
2. Machine Learning Fundamentals
- Definition of ML and its types (supervised learning, unsupervised learning, reinforcement learning). Basic concepts: models, features, overfitting and underfitting. Model evaluation and metrics. Libraries and tools in Python.
3. Supervised Learning
- Linear and logistic regression. Support vector machines (SVMs). Decision trees and random forests. Artificial neural networks (multilayer perceptrons). Practical applications.
4. Unsupervised Learning
- Clustering (K-means, DBSCAN: Density-Based Spatial Clustering of Applications with Noise, Hierarchical). Dimensionality reduction (PCA: Principal Component Analysis, t-SNE: t-Distributed Stochastic Neighbor Embedding). Generative models. Generative adversarial networks (GANs) and VAEs: Variational Autoencoders). Practical applications.
5. Deep Learning
- Introduction to deep neural networks. Architectures: Convolutional Neural Networks. Recurrent Neural Networks. Transformers. Applications in computer vision and natural language processing. Implementation with TensorFlow/PyTorch.
6. Ethics, Bias, and the Future of AI.
The course combines theoretical and practical approaches to ensure comprehensive learning, with an emphasis on the application of artificial intelligence in healthcare settings. It is structured around theoretical lectures, practical sessions, analysis and discussion of real-life AI application cases in healthcare engineering, and the development of a project where students apply the acquired knowledge to solve a specific healthcare problem.
See the electronic folder of the course.
See the electronic folder of the course.
See the electronic folder of the course.
See the electronic folder of the course.