The context for the Statistical Foundations of Business Analytics module is management in complex, interconnected, data-driven environments.

The benefits that...

Statistical Foundations of Business Analytics (MSIN0096)

The module is 1.5CU (45 credits), and it runs through term 1 and 2.
The purpose of this module is to provide students with the broad experience of creating a...

Biomedical Engineering Group Research Project (MPHY0049)

Research Project (Respiratory Clinical Science) (MEDC0103)

QUALITATIVE METHODS AND STRATEGIES FOR RESEARCH IN MEDICAL AND CLINICAL EDUCATIONAL RESEARCH

Module Overview

This module provides an introduction to the use of Qualitative Research in Health Professions Education, supporting students to gain insight...

Qualitative Approaches for Clinical Education (MDSC0021)

LLM SPECIALISATION: INTERNATIONAL BUSINESS LAW

LLM Specialisation: International Business Law (LAWS0188)

LLM SPECIALISATION: NO SPECIALISATION SELECTED

LLM Specialisation: No Specialisation Selected (LAWS0170)

The module is intended as an in-depth study of formal symbolic techniques used to model aspects of human reasoning in a computationally feasible way. A...

Machine Reasoning for Artificial Intelligence (INST0074)

The module is intended as an in-depth study of formal symbolic techniques used to model aspects of human reasoning in a computationally feasible way. A principle aim is to give sufficient theoretical grounding in the areas covered to enable an understanding of current research trends and issues.

On successful completion of the module students will be able to:

construct natural deduction proofs in predicate calculus for short formulae, and check the correctness of longer proofs
	assess the applicability of logic and/or logic programming techniques to represent a range of reasoning tasks and domains
	produce appropriate formal logic-based axiomatisations of simple domains presented informally in English, while identifying ambiguities and logical imprecisions in the informal specifications given
	produce graph-based representations of arguments and their relationships and apply the definitions of abstract argumentation frameworks to evaluate the acceptability of arguments
	describe the differences between the different argumentation frameworks and their relations to other models of reasoning (e.g. nonmonotonic reasoning) and assess their applicability to artificial intelligence and other domains
	assess research articles on topics related to this course
INST0072 Logic and Knowledge Representation is a prerequisite for this module.