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Resources for new students

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Resources for new students

We encourage applications from highly motivated candidates with a background in Engineering, Mathematics, Physics or Computer Science who are interested in biomedical signal processing, image analysis, electronics, wearable technology or machine learning in general. Please explore our areas of research, read our publications and review other resources we have made available to the research community. If you would like to work on challenging projects towards solving an unmet clinical need, please get in touch with us. For general information about the formal application process and entrance requirements, please visit the official University Admissions and the Department of Engineering Science websites.

This page contains information new or prospective students should read before joining our group. It is structured in a way that enables the student to review the material over the summer, before the start of the new academic year on Michaelmas term (see the official Dates of Term). It is expected that the student has sufficient knowledge of the areas covered in this page before starting his/her degree so that the proposed research project advances at a reasonable pace.

Generic minimum requirements

 

Thesis/report writing (LaTeX)

A significant proportion of your research time will be devoted to writing science in a formal academic style. We use the LaTeX typesetting system to write reports, theses, journal papers or other academic documents. You can write your documents from scratch, however, it is often easier to start with an already written LaTeX template. This Git repository contains sample LaTeX templates for a 4th year project (4YP) report and for a DPhil/PhD thesis that our students have used in the past.

There are several software options for writing in LaTex, including online versions such as Overleaf. We typically don't recommend Overleaf, as you will be writing long documents with several figures, tables and other elements. In our experience, having LaTeX installed locally in your computer is a better option. For macOS, you can download the latest MacTeX distribution and use TexPad as your editor (or any other popular editor). In linux, LaTeX is already pre-installed or readily available from your distribution's software repository. Good editors for Linux are Kile and TeXMaker.

  • Linguistics, Style and Writing in the 21st Century
    Prof. Steven Pinker
    Harvard University
    Link to the lecture video
  • Just Enough LaTeX to Survive
    The Math Repository
    Link to the full course video

Tracking changes in source code (Git)

As you advance in your research project, you will need to keep track of the changes you make to your documents and to the source code you develop. You will need to use a version control system. Although viable alternatives exist, Git is the most widely-used option.

  • Lecture 6: Version Control (Git), Fall 2020
    Massachusetts Institute of Technology
    Link to the lecture notes and to the video
  • MIT 6.370: Lecture 4: Git Repository, Fall 2013
    Maxwell Mann and Aaron Epstein
    Massachusetts Institute of Technology
    Link to the syllabus, videos and course notes in MIT Open CourseWare

Computer science

During the course of your degree, you will undoubtedly write code. Regardless of the choice of programming language, you will need to learn some basic concepts on how to structure code and design the correct flow for your proposed algorithms so others can reproduce your findings.

  • MIT 6.0001: Introduction to Computer Science and Programming in Python, Fall 2016
    Dr. Ana Bell, Prof. Eric Grimson, Prof. John Guttag
    Massachusetts Institute of Technology
    Link to the syllabus, videos and course notes in MIT Open CourseWare

MatLab

Matlab is a powerful programming language to rapid-prototype some ideas or quickly explore and visualise some initial results.

If you need extra supporting material, you can review:

  • Introduction to Programming with MATLAB
    Prof. Akos Ledeczi & Prof. Mike Fitzpatrick
    Vanderbilt University
    Link to the tutorial in Coursera

Python

Python is very popular among our students to implement some machine learning algorithms.

  • Learn to Program: The Fundamentals
    Prof. Jennifer Campbell & Prof. Paul Gries
    University of Toronto
    Link to the tutorial in Coursera
  • MIT 6.0001: Introduction to Computer Science and Programming in Python, Fall 2016
    Dr. Ana Bell, Prof. Eric Grimson, Prof. John Guttag
    Massachusetts Institute of Technology
    Link to the syllabus and course notes in MIT Open CourseWare


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4th-year project (4YP) students

Make sure you discuss with your supervisors which of our publications to read and which courses to take during Michaelmas term.

Machine learning

During Michaelmas term, it is expected you will take the relevant courses that will prepare you for your research project. The following resources have been found useful from our past students as introductory material:

Books:

  • "Probabilistic Machine Learning - An Introduction"
    Kevin Murphy
    MIT Press, 2022
    Suggested reading: Parts I, II and III
  • "Pattern recognition and machine learning"
    Christopher Bishop
    Springer, 2006
    Suggested reading: Chapters 1-5

Online courses:

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DPhil students

Discuss with your supervisors which of our publications other any other material relevant to your proposed area of research to read before the start of the academic term.

Machine learning

Although you will develop more advanced skills as your graduate degree progresses, it is expected the student has some basic introductory knowledge in machine learning.

Books:

  • "Probabilistic Machine Learning - An Introduction"
    Kevin Murphy
    MIT Press, 2022
    Suggested reading: Parts I, II and III
  • "Pattern recognition and machine learning"
    Christopher Bishop
    Springer, 2006
    Suggested reading: Chapters 1-5
  • "Information theory, inference, and learning algorithms"
    David MacKay
    Cambridge University Press, 2005
    Suggested reading: Skim over Part V

Online courses:

Linux and BASH scripting

Although you are welcome to use any operating system during your graduate degree, most of the infrastructure in our lab is based on Linux (either Ubuntu or Fedora). Additionally, as part of your research you will be involved in a clinical study and collect data from several medical devices. Before developing your exciting research algorithms, you will need to run several pre-processing tasks to prepare/format your research dataset for analysis. These tasks often include checking the recorded data for consistency, reordering/grouping patient data in a manageable structure, manipulate the contents of a variety of files recorded in several binary or text formats. For this purpose, the tools available in the Linux terminal are very useful.