MSCompletedCo-supervised
2025Attia Noor
Comprehensive diagnostic strategies for thyroid cancer addressing class imbalance with advanced artificial intelligence learning techniques
Teaching
Teaching and supervision
Teaching covers data science, artificial intelligence, and business analytics, with outcome-based education, assessment design, and student-centred practice. Training workshops and other paid or advisory work are listed separately on Services.
Students supervised
MS students I co-supervised. Filter by degree or status, or search a name or topic.
MS (4)
MSCompletedCo-supervised
2025Hamna Zubair
Advanced feature fusion for artificial intelligence driven breast cancer imaging
MSCompletedCo-supervised
2025Khizra Noor
Deep learning empowered framework for improved diagnosis in thyroid carcinoma
MSCompletedCo-supervised
2025Syeda Muntaha Bader
Explainable artificial intelligence for multimodel imaging in breast cancer diagnosis
Modules
Modules I teach, grouped by theme. Each note is a short guide to the subject, not a full syllabus.
Data science
Introduction to Data Science
How data becomes a decision: collecting, cleaning, exploring, and reporting results in plain language.
Data Visualization
Charts and dashboards that a manager can read in a minute, without hiding the story in the spreadsheet.
Predictive Analytics
Using past data to estimate what is likely next, and checking when a prediction should not be trusted.
Data Governance
Who may use which data, how it is stored, and how quality and access are kept under control.
AI and analytics
AI in Business
Where artificial intelligence helps a firm, where it does not, and how to brief a non-technical team.
Business Intelligence
Turning operational records into regular reports that leadership can act on.
Machine Learning for Managers
What a model can and cannot do, so a manager can ask the right questions of a data team.
Applied AI Projects in Business Domains
A full small project: problem, data, model, and a written result a client could understand.
Responsibility
Ethics
Fairness, consent, and harm: the questions that sit beside every dataset and every automated score.
AI Regulation
How rules and organisational policy shape what an AI system is allowed to do in practice.
Computing foundations
Database Systems
Storing and querying structured data so later analysis is reliable rather than improvised.
Information Systems
How people, software, and processes work together in an organisation, not only how a program runs.
Introduction to Computer Applications
Practical digital skills for study and office work: documents, spreadsheets, and everyday tools.
Academic practice
How the teaching is designed, marked, and kept fair.
Curriculum development
Building a course from aims to weekly topics, so students know what they will be able to do at the end.
Outcome-based education (OBE)
Teaching and marking against stated outcomes, not only against a list of chapters.
Assessment design and moderation
Tasks and marks that match the outcomes, checked so grading stays consistent across markers.
Academic quality assurance
Reviewing a module so it stays current, fair, and aligned with departmental standards.
Student-centred learning
Class time used for practice and feedback, not only for one-way lectures.
Inclusive teaching practices
Materials and assessment that more students can actually complete, including those who start with less background.
Higher education instruction
University teaching across undergraduate and professional modules in computing and data.