Why this mini-credential matters
Connect applied machine-learning workflows, forecasting and anomaly detection with digital-twin scenarios and curriculum-aware learning agents.
The course examines escalation, accessibility, learning analytics and the limits of automated guidance rather than treating an AI assistant as an unsupervised teacher.
By the end
What you should be able to do
- Prepare data for a simple monitoring or forecasting task.
- Interpret an AI-enabled digital-twin result.
- Design a learning-agent or tutor escalation workflow.
- Identify accessibility, bias and human-oversight requirements.
Learning path
Indicative syllabus
- 01
Engineering data and applied ML foundations
- 02
Forecasting, monitoring and anomaly detection
- 03
Digital-twin scenario design
- 04
Learning agents and curriculum-aware support
- 05
Analytics, accessibility and human escalation
Assessment evidence
How learning is demonstrated
Notebook or simulation exercise, agent/escalation scenario and short evidence reflection.
Credential and recognition
Successful completion may lead to a STEMMING/project or delivering-HEI credential under the approved procedure for that cohort. EIT Label, ECTS equivalence and local recognition are not automatic and must be confirmed before enrolment.
The delivery owner must confirm the issuer, local recognition, any ECTS equivalence and final co-branding before recruiting a cohort.