Translation and Intercultural Dialogue (ELCS0044)

Dissertation on MSc Engineering &amp; Education

Engineering and Education Dissertation (EDPS0224)

Dissertation on MSc Engineering & Education

This module is compulsory and needs to be taken by all students completing MClinDent in Orthodontics and MClinDent in Orthodontics (AT).

MClinDent in Orthodontics: Dissertation (DENT0067)

IN-DEPTH RESEARCH|ASSESSMENT RESEARCH|RESEARCH SKILLS|MASTERS THESIS

This is the dissertation module for the MA Educational Assessment. Please register for the spring term (Terms 2 and 3) if you wish to complete for September...

Dissertation on Educational Assessment (CPAS0070)

This is the dissertation module for the MA Educational Assessment. Please register for the spring term (Terms 2 and 3) if you wish to complete for September 2023.

Artificial Intelligence|statistical learning theory|machine learning

Aims:

The module aims to empower the students with the mathematical framework, tools, and reasoning to analyse and understand how machine learning algorithms...

Foundations of Artificial Intelligence (COMP0186)

Summary

This module is intended for students who wish to develop their knowledge and understanding of  the principles of immunisation and communicable...

Immunisation and Communicable Diseases (CHLD0059)

Module Ouline:

This module aims to provide students on MSci/BSc Chemistry with Management or Chemistry with a European Language without an A or AS level in...

Introduction to Quantitative Chemistry (CHEM0003)

Module Ouline:

This module aims to provide students on MSci/BSc Chemistry with Management or Chemistry with a European Language without an A or AS level in maths with the required level of mathematics for the remainder of the chemistry degree programme.Module Aims:

At the end of the module students should be able to:
1. Understand how to present mathematic workings and solutions.
2. Understand and use the fundamentals of algebra.
3. Differentiate and integrate functions of one variable.
4. Perform statistical analysis of data sets.Teaching and Learning Methods:

Lectures: The topics will be introduced through a blended delivery of asynchronous videos with online activities, and support hours.

Workshops are an opportunity to receive feedback on your understanding of the module material, hence preparation and active participation is essential if you wish to derive the most benefit. Workshop attendance is monitored, but not compulsory.

Self-study: In addition to timetabled hours, it is expected that you engage in self-study in order to master the material. This can take the form of practicing example questions (workshop questions, additional questions available on Moodle, worked examples from lectures), completion (and redoing under exam conditions) the set coursework, and further reading in textbooks and online.