Anglia Ruskin University × China Jiliang University

Meaning.
Learning.
Intelligence.

A shared learning portal connecting semantic technologies, advanced machine learning and deep neural networks.

Cohort: 25 studentsFormat: lectures · tutorials · practicalsAcademic year: 2026
Meet the cohortExplore the modules
Course overview

Understanding intelligent systems

Three modules trace one arc: from explicit meaning and graph structure, through statistical learning, to deep neural representations.

01Represent

Describe entities, relationships and meaning.

02Connect

Build graphs that integrate heterogeneous data.

03Learn

Identify patterns through statistical models.

04Reason

Combine evidence, rules and learned representations.

05Apply

Build trustworthy systems for real problems.

Module pathways

One programme, three perspectives

Each module has its own course-outline page. Together they form a progression from formal knowledge to adaptive learning systems.

01

Semantic Data Technologies

RDF, RDFS, OWL, SPARQL, WebProtégé, SHACL and knowledge graphs.

View course outline →
02

Advanced Machine Learning

Experimental design, ensembles, kernels, uncertainty, graph learning and responsible ML.

View course outline →
03

Neural Networks & Deep Learning

Backpropagation, CNNs, transformers, generative models, GNNs and deployment.

View course outline →
Beyond the modules Our Stories: learning in action Explore experiences and discoveries from across our community, from a robotics competition in Jingzhou to the Four As of frontier AI governance.
Explore stories
Our global learning community

Visitors around the world

From our classrooms to an international audience, explore how people connect with the ARU–CJLU learning portal.

01 / Homepage views

Every visit counts

Live page views since the counter was added.

Live count of homepage page views, including repeat visits

Includes repeat visits. Earlier traffic is not included.

Figures update through independent counting services and may differ slightly. Locations represent detected visitor countries, not nationality.

People make the programme

Discover the students, instructors and tutors who shape this learning community.

25 student profilesTeaching team