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Available courses

Neural Networks and Deep Learning for AAL

The main goal of this course is to introduce students to the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

Machine Learning in AAL

Do you want to become a master in machine learning?.Then start today!, it would take you four hours to learn the basics of Machine learning. Imagine, for the time you spend watching youtube useless videos you can learn how they built their recommendation system. How do you get commercials for hotels after searching them on booking? Is this question still a mystery, then you need an  introduction to machine learning.

Stream processing in Apache Flink for AAL

Introduction to the concepts of processing of stream data

Introduction to Python

Python is a general-purpose, versatile, and powerful programming language. It’s a great first language because it’s concise and easy to read. Whatever you want to do, Python can do it. From web development to machine learning to data science, Python is the language for you.

Data VIsualuzation

Introduction to the concepts of AI and HPC with application to AAL with practical examples in Python

Datasets and preprocessing in AAL

The main goal of this course is to introduce students to some of the most important and widely used preprocessing techniques, as well as the tools they can use to apply them to AAL datasets.

Explainable AI in AAL

The main goal of this course is to introduce students to techniques that can be used to explain Artificial Intelligence models, with a specific focus on their application on models that analyze and classify data from AAL systems. It also aims to demonstrate the Python tools and libraries that can make this application easier and available to a wide variety of machine learning models

AI and HPC in AAL

Introduction to the concepts of AI and HPC with application to AAL with practical examples in Python

Smart Housing and AAL Principles

The objective of the unit is to introduce the concept of AAL to the students. The students will be introduced to the advanced concepts of sensor data
acquisition and processing from both hardware and software point of view.

Students will learn thoroughly the concepts of sensing and smart environments and be able to apply these concepts in the design and building phases.
Students will learn how to overcome the challenges in the design and implementation of a successful
ambient assisted living system.

Architecture and Furniture Design
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Interactions Between BIM, Smart Housing And AAL

The course unit prepares the participants to combine the specific requirements of Ambient Assisted Living with the requirements of the BIM method.

Participants will use their AAL knowledge to develop meaningful and workable design proposals for the living environments of older people. The unit provides student with transversal knowledge in fields of BIM, Smart Housing and AAL.

Medical Sciences
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Needs of senior citizens and their caretakers

The objective of the unit is to prepare building designers, construction workers, and related professionals with knowledge, skills and competences required to make design decisions that support healthy active ageing, and to implement those solutions in new buildings and refurbishments.

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Project Management, Innovation Management And Collective Skills for an Optimum Implementation of BIM Principles and AAL Concepts

The objective of the unit is to train the learners in project management related to BIM, which includes innovation management, information management, working with transversal groups or cross-cultural competencies. The central aspect of the unit is management the information provided by BIM and its teamworks and how to assess this information to obtain quality outputs.