Ebube Glory Ogbonda

Introduction to Deep Learning neural networks and their applications.
A2 - A collection of small problems based on the second half of the module. Hand-crafted solutions and short Python code solutions are expected.

Apply information visualization techniques (Representation, Presentation, and Interaction) to develop new visualization techniques.
Critically evaluate Information Visualization techniques and suitability for a given purpose.
Describe different aspects of data, identify, analyse and evaluate component parts.
Evaluate and develop alternative visualization design solutions through sketching and planning.
Select appropriate visual techniques for a particular context.
Storytelling… Show more

Apply information visualization techniques (Representation, Presentation, and Interaction) to develop new visualization techniques.
Critically evaluate Information Visualization techniques and suitability for a given purpose.
Describe different aspects of data, identify, analyse and evaluate component parts.
Evaluate and develop alternative visualization design solutions through sketching and planning.
Select appropriate visual techniques for a particular context.
Storytelling and Presentation. Display, layout and presentation of visual information. Interaction and static presentation. Techniques such as focus-and-context, size adjustment, scale and distortion; multiple views and composite displays.
A3-Imp: - Created visualisation, presented outcome, and reflective report Show less

Appreciate professional, legal, social, cultural and ethical issues.
Critically analyse and select from appropriate research methodologies.
Devise research objectives, develop research design and plan research activities.
Plan a literature review; write a short research summary underpinning project rationale.
ClassTest - Research Methods Case Study Open Book Class Test

Primary data collection methods.
Statistical principles of data analysis.
Summarising results and arriving to meaningful conclusions.
Preparing research proposals.
Professional, legal, social, cultural and ethical issues related to computing and an awareness of societal and environmental impact of research projects.

CW2 - Methodology proposal

XR Application Programming & Development
Final Assessment: Implement - XR Application Implementation, Evaluation and Reporting

Review of the fundamentals of machine learning: supervised and unsupervised learning, classifier models, and feature selection.
Classifier ensembles.
Imbalanced data, streaming data and concept drift: machine learning solutions.
A1 - A collection of small problems based on the first half of the module. Hand-crafted solutions and short Python code solutions are expected.

Research theory basics: paradigms, domains and approaches.
Contextualisation of Research Methods in relation to MSc project scope.
Research process: design, planning, and implementation.
Coping with information overload, selection of secondary sources, their critical evaluation and role in independent research. Structuring literature review to inform theoretical framework and justify selection of methodology.
Research rationale; setting research objectives and developing… Show more

Research theory basics: paradigms, domains and approaches.
Contextualisation of Research Methods in relation to MSc project scope.
Research process: design, planning, and implementation.
Coping with information overload, selection of secondary sources, their critical evaluation and role in independent research. Structuring literature review to inform theoretical framework and justify selection of methodology.
Research rationale; setting research objectives and developing hypotheses.
CW1 - Who, what, when, where and why? Show less

Registration and tracking Mechanisms for XR
User Experience of XR applications
Presence and Immersion
A-2: Reqs - XR Application Requirements Outline

Design of visualisations, Five Design-Sheet method; Consideration and critical thinking around alternative solutions.
A-2-Design: - Technical design plan

Foundational principles of (X)R (Virtual Reality, Mixed/Augmented Reality) interaction paradigms and related environments.
History, state-of-the-art and Future of (X)R
XR Interfaces, services and hardware platforms
A-1: Literature Review Outline (mind-map)

Clustering.
Neural networks: standard architectures and deep learning.
CA-2: A collection of small problems based on the second half of the module. Hand-crafted solutions and short Python code solutions are expected.

Detail and apply clustering algorithms to data sets.
Evaluate classifier models and their operation using appropriate metrics and measures.
Explain and apply the basic notions and principles of machine learning.
Summarise neural network models and their training procedures.
Understand feature selection methods.
Exam: 2-hour exam of type "Choose any 2 of 4", consisting of problems to solve by hand. The problems will be similar to those in the labs and assignments. All notes… Show more

Detail and apply clustering algorithms to data sets.
Evaluate classifier models and their operation using appropriate metrics and measures.
Explain and apply the basic notions and principles of machine learning.
Summarise neural network models and their training procedures.
Understand feature selection methods.
Exam: 2-hour exam of type "Choose any 2 of 4", consisting of problems to solve by hand. The problems will be similar to those in the labs and assignments. All notes, books, and internet resources are NOT permitted. Show less

Data; the definitions and concepts.
Contextualisation of dat

Analytical methods using data.
Ethics of processing data with and without automatic decisions.
Data Protection legislation (DPA, GDPR, HIPAA, COPPA, etc.).
Data collection mechanisms.
Storage mechanisms for data (in broad terms)

History and future of Information Visualization; the challenges of Information Visualization; tasks; user, perception, data types.
Looking at data. Data capture and problems of capturing data. Selection/abstraction of data (aggregation, sampling; binning; cropping); Big data challenges.
Understand current visualisation techniques, including traditional plots (bar, line, scatter etc.), parallel coordinate plots, treemaps, re-orderable matrix; scatter plot matrix.
Perception, visual… Show more

History and future of Information Visualization; the challenges of Information Visualization; tasks; user, perception, data types.
Looking at data. Data capture and problems of capturing data. Selection/abstraction of data (aggregation, sampling; binning; cropping); Big data challenges.
Understand current visualisation techniques, including traditional plots (bar, line, scatter etc.), parallel coordinate plots, treemaps, re-orderable matrix; scatter plot matrix.
Perception, visual mapping and interpretation; understanding how humans perceive information. Encoding of value; Encoding of relation; Models: Bertin, Mackinlay (Quantitative, Ordinal, and Categorical), Semiotics.
A-1: - Analysis of data Show less

Basics of machine learning: Concepts of object, class, feature. Training and testing protocols. Error estimation. ROC curves. Supervised and unsupervised learning.
Classification methods: basic classifiers and classifier ensembles.
Feature selection
CA-1: A collection of small problems based on the first half of the module. Hand-crafted solutions and short Python code solutions are expected.

Semester Four (4) Professional Diploma in Information Technology

Semester Four (4) Professional Diploma in Information Technology

Semester Four (4) Professional Diploma in Information Technology

Semester Four (4) Professional Diploma in Information Technology

Semester Three (3) Advanced Diploma in Information Technology

Semester Three (3) Advanced Diploma in Information Technology

Semester Three (3) Advanced Diploma in Information Technology

Semester Two (2) Honours Diploma in Information Technology

Semester Two (2) Honours Diploma in Information Technology

Semester Two (2) Honours Diploma in Information Technology

Semester Two (2) Honours Diploma in Information Technology

Semester One (1) Advanced Certificate in Information Technology

Semester One (1) Advanced Certificate in Information Technology

Semester One (1) Advanced Certificate in Information Technology

Semester One (1) Advanced Certificate in Information Technology