Modular Training Courses

COM6509 Machine Learning and Adaptive Intelligence

Open to:
Faculty of Engineering

Staff Contact:
Prof. N Lawrence

When Taught: Autumn Semester

The module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real world phenomena. The module will give students a grounding in modern state of the art algorithms that allow modern computer systems to learn from data.

Academic Aims:
This unit aims to provide a deep understanding of the fundamental technologies underlying modern artificial intelligence. In particular it will provide foundational understanding of:

probability in artificial intelligence,
supervised learning for classification and regression,
unsupervised learning for data exploration.

Learning Outcomes:

  • give a general understanding of probability theory and how it relates to uncertainty in modern artificial intelligence,
  • understand when and how to implement the appropriate learning paradigm for a given application,
  • have a deep understanding (including how to implement) of a range of supervised and unsupervised learning algorithms. Potential examples include: linear regression, linear classification, naive Bayesian classification, principal component analysis, k-means clustering, decision trees.

Have a broad understanding of more complex technologies (i.e. when they are applicable, and how they are more powerful than simpler techniques). Potential examples include: the support vector machine, kernel methods, probabilistic graphical models, E-M algorithms, factor analysis, nonparametric Bayesian methods.

HAR6035 Introduction to Statistics and Critical Appraisal

Open to: Faculty of Medicine, Dentistry & Health

Staff Contact: Professor Stephen Walters

When Taught: Autumn Semester

The unit introduces students to basic concepts and techniques such as hypothesis testing and confidence interval estimation in statistics. Students will learn some simple statistical methods and the principles behind some advanced methods such as regression. It will equip students with the knowledge and skills necessary to understand and critically appraise statistics in research literature.

The course is not aimed at ‘doers’ of statistics, that is, students who are going to design their own studies to collect and analyse their own data. It will not teach you how to analyse, present and report your own data.

Academic Aims:

  • To introduce students to fundamental concepts and methods in medical statistics,
  • To enable students to apply these concepts to critically appraise research literature.

Learning Outcomes:

By the end of the unit, a student will be able to:

  • Classify and appropriately display and summarise different types of data,
  • Describe the properties of the Normal distribution,
  • Distinguish between a population and a sample, and describe the precision of a sample estimate of a population parameter,
  • Explain the concept of confidence intervals as applied to means, proportions, differences in means, and differences in proportions
  • Describe the process of setting and testing statistical hypothesis,
  • Distinguish between `statistical significance’ and `clinical significance’,
  • Evaluate the quality of published research
HAR6042 Introduction to Statistics and Critical Appraisal Online

Open to: University-wide

Staff Contact: Dawn Teare

When Taught: Spring Semester

The unit, which is delivered online, introduces students to basic concepts and techniques such as hypothesis testing and confidence interval estimation in statistics. Students will learn some simple statistical methods and the principles behind some advanced methods such as regression. It will equip students with the knowledge and skills necessary to understand and critically appraise statistics in research literature.

Academic Aims:

  • To introduce students to fundamental concepts and methods in medical statistics,
  • To enable students to apply these concepts to critically appraise research literature.

Learning Outcomes:

By the end of the unit, a student will be able to:

  • Classify and appropriately display and summarise different types of data.
  • Describe the properties of the Normal distribution.
  • Distinguish between a population and a sample, and describe the precision of a sample estimate of a population parameter.
  • Explain the concept of confidence intervals as applied to means, proportions, differences in means, and differences in proportions.
  • Describe the process of setting and testing statistical hypothesis.
  • Distinguish between `statistical significance’ and `clinical significance’.
  • Evaluate the quality of published research.

HAR6045 Further Statistics for Health Science Researchers

OR

HAR6021 (online version)

Open to: University-wide

Staff Contact: Dr Jeremy Dawson

When Taught: Spring Semester

The unit covers fundamental statistical concepts, and both simple statistical methods and the more widely used advanced methods of multiple regression, survival analysis and generalised linear models. It will be a practical module, including the teaching of the statistical software SPSS, equipping students with the knowledge and skills necessary to design and analyse a study to answer specific research questions; to understand and critically appraise the literature; and to present research findings in a suitable fashion.

Academic Aims:

  • introduce students to fundamental concepts and analysis methods in statistics used by health science researchers.
  • enable students to apply these concepts to critically appraise research literature.
  • equip students with the knowledge and skills necessary to appropriately analyse a study using SPSS; and to present research findings in a suitable fashion.

Learning Outcomes:
By the end of the unit, a student will be able to:

 Classify and appropriately display and summarise different types of data..

  • Describe and test statistical hypotheses in an appropriate manner.
  • Analyse data appropriate to the particular study design.
  • Understand parametric and non-parametric tests and when they should be used
  • Understand how to use multiple linear regression.
  • Understand how to use logistic regression and other generalised linear models.
  • Understand how to use survival analysis.
  • Use SPSS to perform all of the above analyses and to manage data.
  • Evaluate the quality of published research from recent papers.

NB: These outcomes relate to the following QAA subject-specific skills in health studies: the ability to understand, interpret and critically appraise the statistical information presented in the health and health care literature; the ability to draw on research and research methodologies to locate, review and evaluate research findings relevant to health and health issues, across a range of disciplines.

PLEASE NOTE: There are two versions of this course, one which is taught on campus (HAR6045) and one online (HAR6021). Please only register for HAR6045 if you are willing and able to attend all classes in person as timetabled. If you primarily wish to access the materials, you should register for HAR6021. 

Short Courses and Other Resources on Statistics

First Steps to NVIVO

This course is designed for users to be able to create an NVivo document, introduce their sources to NVivo and start annotating and coding their sources. LEARN MORE

Next Steps to NVIVo

This course is designed for users to be able to use some of the complex tools that are available in NVivo: to help them with coding, specialist analytic techniques and reporting to others. LEARN MORE

Getting Data into SPSS

For users to be able to open SPSS, set up a data set, manually enter a data set and also enter data from other packages e.g. Excel. LEARN MORE

Going Further with SPSS

To introduce users already familiar with basic SPSS to some of the most commonly used advanced statistical techniques in SPSS. LEARN MORE

Simple Graphs & Statistics with SPSS

For users to be able to explore their data graphically, carry out simple descriptive analyses and bi-variate analyses. LEARN MORE

Help! I need to use 'R'

To enable researchers to make an assessment of whether they want to use R. LEARN MORE

MASH - Maths & Stats Help

For further help with choosing the correct method book a 1:1 session with School of Mathematics and Statistics. LEARN MORE

Visualising & Analysing Biomedical Datasets with R

This unit will teach students how to visualise biomedical data using histograms, scattergrams, line plots, and more sophisticated methods including heatmaps, and how to turn these into publication-quality images. LEARN MORE

Looking After Your Research Data

This workshop will help you to get the most out of your data by keeping it safe and secure, organising and describe it, archiving it and sharing it, and by augmenting it with pre-existing data from a variety of sources. You will have the opportunity to put what you learn into practice by starting a data management plan for your own research.  LEARN MORE

Research Impact: Sharing Your Research

This workshop will introduce the principles of Open Access publishing. We will explore the benefits of sharing your research, and discuss how this can boost the impact of your research outputs and raise your profile. We will introduce ways to share your research publications and data, and demonstrate the University’s open access repositories.  LEARN MORE

Introduction to Basic Statistics for Biologists

OPEN TO: Faculty of Science

YEAR OF STUDY: All Years

There are actually very few statistical tests that scientists (as compared to statisticians) use. A few can be done by hand: these include the two most common ones (t-test and chi-squared test). In addition, most of the common calculations (mean, standard deviation, t-test) can be done in Excel. These are all very simple with a little guidance. This short module is designed to give the basics to students with little prior knowledge of statistics. It also serves as a preparation for the more advanced DDP modules available (e.g. APS6060 - Advanced Statistics for Biologists).

AIMS:
• Explain how to choose a relevant test
• Go through the really common tests
• Look at your results, and see how statistics could help

LEARNING OUTCOMES:
Students will receive basic training in statistics for biologists, essential to all aspects of biological research and required for participation in advanced statistics training provided by the DDP.

TEACHING METHODS:
The course is delivered in a single session through interactive lecturing and worked examples.

ASSESSMENT METHODS:
Attendance will be monitored but no formal assessment will be made. However, feedback will be given during each session and at the end of the practical exercises. Strong positive feedback will be required for progression to advanced courses.

LEARN MORE