The University of Sheffield
Neil Lawrence ML@SITraN

Neil Lawrence's Available Projects

NDL-1 Psychoc: The Artificial Psychic
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Abstract

In this project we will build an artificial psychic called psychoc. The artificial psychic will work by querying a user on preferences about life (e.g. movies) and making predictions about what type of person the user is. Psychoc could either be a web interface or a mobile phone app, but the main initial task will be to build psychoc's information engine. Initially psychoc won't be a very good artificial psychic (its information engine will be a little rusty), but over time psychoc should 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.

This project will suit students with strong analytical skills, there will be a focus on linear algebra and probabilistic inference in the software.

NDL-4 Monitoring Aerodynamic Performance Real Time for Cycling
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Abstract

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.

NDL-7 Gesture Recognition using Kinect and Python
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Abstract

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.

NDL-5 Machine Learning for Fitness Monitoring with Python
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Abstract

Technologies that were previously only available to elite athletes are becoming widespread. Now casual athletes can buy systems that monitor pace, heart rate and other information for under 300 pounds. This project will build a software tool for analysis of data of this type. 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.

NDL-8 Learning Depth Perception using Kinect and Python
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Abstract

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.

NDL-2 Verifying Identity
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Abstract

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.

NDL-3 Machine Learning for Modelling Formula One Races
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Abstract

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.

NDL-6 Motion Capture Data Modelling in Python
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Abstract

The Python programming language is becoming a de facto standard for implementation of machine learning algorithms. This project will develop tools for modelling of motion capture data in the Python programming language. Based on existing tools in MATLAB, the aim of the project will be to port the underlying machine learning techniques to the more powerful Python programming language. The end aim is to provide a simple tool for animators to model motion capture data and create new animations for computer games or the film industry.

A previous project has built a visualization framework for motion capture data, this year's project will focus on more advanced machine learning methodologies. The student will work with an ongoing software development framework being developed by the machine learning group. 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.

Generated on Monday, February 27, 2017 at 18:39:58