The course covers theoretical, system, and application aspects pertaining to the use of mobile, wearable, and the Internet of Things devices (herefrom referred to as “ubiquitous”) for sensing and learning about the environment. The course starts with the overview of ubiquitous sensing platforms, covering topics such as the constraints and applications of these platforms, and the functioning of these platforms, thus touching upon the sampling theory (including the recent advances in sub-Nyquist sampling) and the "sampling - feature extraction - machine learning" pipeline. The course then thoroughly examines recent innovations that finally brought deep learning (DL) to a range of ubicomp devices, before continuing with an in-depth investigation of applications of DL on this platform, e.g. for human activity recognition, healthcare, authentication and security, and wireless inference. Deep learning is also in the focus of the collective intelligence brought by distributed IoT deployments. The course thus covers distributed DL training via federated and split learning, and solutions for local-cloud learning distribution. A key component of the course is a practical project that students will independently work on. The project harnesses modern tools for mobile sensing (e.g. Android) and on-device deep learning (e.g. TensorFlow Lite) and requires students to develop a full-fledged mobile deep learning application. Student participation is facilitated further by mandatory research paper presentations that will be delivered by each student in the class. Finally, the course's relevance for preparing students to address global challenges is exacerbated through a dedicated lecture on ubicomp technologies for tackling COVID-19 pandemics and a guest lecture by Prof. Mariya Zheleva, State University of New York at Albany, USA, on mobile solutions for developing regions.
- nosilec: Pejović Veljko