Readings:
Lectures
Lectures are in person (every Monday) - Lectures will be also available online (Zoom link) (Passcode: pds2021)
Lectures will take place following the (tentative) schedule below.
- 02.09 Introduction to Pervasive Data Science (Slides)
- 09.09 Project selection (online)
- 16.09 How to develop a testbed with rapid prototyping equipment"
- Gregor Rehand - Time series into images for DL (Deep Learning)
- Joosep Näks - Quality of pedestrian roads using micro-mobility vehicles
- 30.09 Lecture "Experiences from previous students (Reo Kuchida) and Examples of testbeds of other successful projects (Slides)
- 07.10 Lecture "PhD student presentation - HIPPO: Pervasive Hand-grip Estimation from Everyday Interactions" - Presentation delivered in ACM UbiComp 2023 (Slides)
- 14.10 Lecture "Statistical analysis over data''' (Slides)
- 21.10 Student checking point 2 and Lecture "PhD student presentation - Seamless integration of light sensors with nanodrones for produce quality estimation" - Presentation delivered in ACM IoT 2023 (Slides)
- 28.10 No-session
- 04.11 PhD student presentation - Upscaling Fog Computing in Oceans for Underwater Pervasive Data Science using Low-Cost Micro-Clouds - Presentation delivered in ACM SenSys 2023 (Slides) and Lecture "How to present my research in a conference" (Slides)
- 11.11 Student checking point 2
- 22.04 Lecture "How to perform a (concise) literature review?" (Slides)
- 18.11 Student project development (no-session)
- 25.11 Presentation and agreement of final deliverables (Slides)
- 02.12 onward - Final presentations
Course is graded Pass or Fail based on deliverable quality (if not possible attending in-person meetings, please contact the lecturer at Room 3045, Delta)