Introductory description

In the context of continuous improvement within Artificial Intelligence (AI) systems and processes, the utilisation of advanced statistical tools, tailored for the dynamic AI landscape, proves instrumental in facilitating progress from foundational enhancements to achieving excellence. This module covers statistical methods essential for effective data modelling in AI. It describes the application of statistical techniques for comprehensive data analysis and interpretation. Additionally, it explores some of the tools for investigating processes, aiming either to solve specific problems or to gain insights that can shape future development and enhancement strategies.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Statistical models and "real world" systems.
Statistical Distributions (Continuous and Discrete).
Exploratory Data Analysis.
Sampling and Inference.
Analysis of Variance (ANOVA).
Regression (linear and non-linear) and Correlation
Time-series Analysis.

Learning outcomes

By the end of the module, students should be able to:

• Collaboratively apply statistical tools and methods tailored to specific scenarios, present robust statistical solutions, identify and address challenges and provide insightful recommendations for decision making.
• Evaluate the assumptions and assess the appropriateness of different statistical models for diverse scenarios, incorporating AI techniques to enhance model selection and performance evaluation.
• Apply advanced statistical tools and techniques to solve problems in real-world systems.
• Critically interpret the quantitative data analysis results, present them in a meaningful way for decision making and provide practical recommendations.
• Demonstrate advanced practical skills in implementing statistical analyses using applicable software packages and AI-driven tools to automate data processing and analysis, and illustrate the outcomes through clear visualisations.

Indicative reading list

Please refer to the Talis Aspire link.

View reading list on Talis Aspire

International

Topics are of high international demand

Subject specific skills

Students are expected to gain/improve on the following:
Data management and statistical analysis.
Clear mathematical communication.
Advanced quantitative reasoning.
Statistical modelling.
Use of software to support decision making.

Transferable skills

Analytical skills, Problem-solving, Communication, Organisation, Teamwork, Flexibility