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Department of Computer Science and Technology



Course pages 2023–24

Mobile, Wearable Systems and Machine Learning

Principal lecturer: Prof Cecilia Mascolo
Additional lecturer: Dr Yang Liu
Taken by: MPhil ACS, Part III
Code: R269
Term: Michaelmas
Hours: 16
Format: In-person lectures
Class limit: max. 22 students
Moodle, timetable


This module aims to introduce the latest research advancements in mobile systems and mobile data machine learning, spanning a range of domains including systems, data gathering, analytics and machine learning, on device machine learning and applications such as health, transportation, behavior monitoring, cyber-physical systems, autonomous vehicles, drones. The course will cover current and seminal research papers in the area of research.


The course will consist of one introductory lecture, seven two-hour, and one three-hour, sessions covering a variety of topics roughly including the following material (some variation in the topics might happen from year to year):

  1. System, Energy and Security
  2. Backscatter Communication, Battery Free and Energy Harvesting Devices
  3. New Sensing Modalities
  4. Machine Learning on Wearable Data
  5. On Device Machine Learning
  6. Mobile and Wearable Health
  7. Mobile and Wearable Systems of Sustainability

Each week, three class participants will be assigned to introduce assigned three papers via 20-minute presentations, conference-style and highlighting critically its features. Each presentation will be followed by 10 minutes of questions. This will be followed by 10 minutes of general discussion. Slides will be used for presentation.

Students will give one or more presentations each term. Each student will submit a paper review each week for one of the three papers presented except for the week they will be presenting slides. Each review will follow a template and be up to 1,000 words. Each review will receive a maximum of 10 points. As a result, each student will produce 6-7 reviews and at least one presentation, probably two. All participants are expected to attend and participate in every class; the instructor must be notified of any absences in advance.


On completion of this module students should have an understanding of the recent key research in mobile and sensor systems and mobile analytics as well as an improved critical thinking over research papers.


  • Aggregate mark for 7 assignments from 6-7 reports and 1-2 presentations. Each report or presentation will contribute one seventh of the course mark.
  • A tick for presence and participation to each class will also be awarded.

Recommended reading

Readings from the most recent conferences such as AAAI, ACM KDD, ACM MobiCom, ACM MobiSys, ACM SenSys, ACM UbiComp, ICLR, ICML Neurips, and WWW pertinent to mobile systems and data.