MSc Projects
These projects are run by the open data science initiative. Different projects will be suitable for different students. If you wish to do a project with us then up to our capacity limit we will find a suitable project for you, but we cannot guarantee that all projects will be suitable for all students.
'Skill Learning': Analytics with Big Data
Supervisor: Tom Stafford Deparment of Psychology
DSI Academic: Neil D. Lawrence
I have access to large existing data sets which contain the potential to show skill development on real-world tasks for large numbers of people (i.e. n>1,000,000 in domains of chess and online maths education). Using theory from the cognitive science of learning, advanced statistical models and open source programming languages (R, Python) we will test theories of what makes learning most effective. The ambition will be to design more effective learning practices.
Stafford, T. & Dewar, M. (2014). Tracing the trajectory of skill learning with a very large sample of online game players. Psychological Science, 25(2) 511-518.
Verifying Identity
Supervisor: Michael T. Smith
DSI Academic: Neil D. Lawrence
How can you tell if a user is who they say they are? How can you tell if they are a real person or a bot? Can you do it without having the user reveal their inforamtion to you. An individual has the right to privacy, but what if they abuse that right to commit fraud? In this project (in collaboration with a start up company) we will consider how machine learning can be used to balance the need of the individual for privacy agains the need of society to be able to validate identity. Our aim is to build distributed user indenity validation systems that do not require the user to reveal personal information. We will do this by designing intelligent, machine learning based, agents that validate a user’s information locally on the telephone. The project may involve collaboration with a London based start up company operating in this area.
This project will suit students with strong analytical skills, there will be a focus on linear algebra and probabilistic inference in the software.
Scikic: The Artificial Psychic
Supervisor: Michael T. Smith
DSI Academic: Neil D. Lawrence
In this project you will contribute to an artificial psychic called Scikic (scikic.org). The artificial psychic works by querying a user on preferences about life (e.g. movies) and making predictions about what type of person the user is. Scikic consists of a front end (a web interface or a mobile app), and a back end (an information engine). At the moment Scikic isn’t a very good artificial psychic (its information engine is a little rusty, it doesn’t have enough data), but over time Scikic will be able to make good predictions about people using only a little information. Software for the project will be written according to the principles of open data science.
The project is a collaboration with the start up company CitizenMe.
This project could suit students with strong analytical skills: for the inference engine there will be a focus on linear algebra and probabilistic inference in the software. However, we also need students with a good knowledge of web interfaces and a flair for design.
Monitoring Aerodynamic Performance Real Time for Cycling
Supervisor: Neil D. Lawrence
A cyclist’s aerodynamic position has a very strong affect on their performance. In professional cycling, extensive wind tunnel testing is used to hone a cyclist’s performance. Such testing is, however, highly expensive. In this project you will use machine learning techniques alongside the physics of cycling to estimate the aerodynamic performance of a cyclist real time whilst on a bicycle. By combining an anemometer, a power meter and an understanding of the rider’s kinetic and gravitational potential energy the power loss due to aerodynamic drag can be estimated. Software for the project will be written according to the principles of open data science.
Note that field experiments will require access to a road bicycle (power loss due to rolling resistance on a mountain bicycle is too large) and some form of GPS device (for preliminary experiments a smart phone is likely sufficient).
This project will suit students with strong analytical (mathematical) skills.
Machine Learning for Modelling Formula One Races
Supervisor: Javier Gonzalez
DSI Academic: Neil D. Lawrence
The machine learning group is working with one of the leading forumla one teams in analysis of data generated in Formula One races with the aim of improving strategy. With this aim we are running one or more projects this year focussed on Formula One data. Formula one is a data intensive sport, information about the location of each team’s car during the race is provided to the teams. Optimization of pit stop strategy can make the difference between winning and loosing the race.
There are commercial confidentiality issues over which areas will be studied, but interested students can discuss these areas directly with Professor Lawrence.
This project will suit students with strong analytical skills, there will be a focus on linear algebra and probabilistic inference in the software.
Learning Depth Perception using Kinect and Python
Supervisor: Zhenwen Dai
DSI Academic: Neil D. Lawrence
Kinect cameras provide true image and an associated depth image. These two images are providing different information, yet a human can infer depth directly from an image. This project will focus on using machine learning techniques building on the machine learning groups python code to see what can be learnt about depth from images. The ultimate aim will be to reconstruct the depth in a real image by learning about depths from information provided by the Kinect camera. Software for the project will be written according to the principles of open data science.
This project will suit students with strong analytical skills, there will be a focus on linear algebra and probabilistic inference in the software.
Gesture Recognition using Kinect and Python
Supervisor: Andreas Damianou
DSI Academic: Neil D. Lawrence
Kinect cameras provide true image and an associated depth image. In this project the focus will be on data from the Gesture Recognition Challenge for kinect: http://www.kaggle.com/c/GestureChallenge/. The student will participate in the challenge using state of the art machine learning techniques with the assistance of the Sheffield Machine Learning group. A gesture recognizer for the Kinect would enable a large range of new interfaces between the human and computer. Software for the project will be written according to the principles of open data science.
This project will suit students with strong analytical skills, there will be a focus on linear algebra and probabilistic inference in the software.
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