01 / Start with the right foundations
Review the prerequisite courses and readiness requirements for your chosen route. Prior experience can be considered through an evidence-based review; it is not an automatic exemption. Advanced robotics and Physical AI routes expect substantial preparation.
02 / Connect concepts to guided practice
Each course combines planned taught activities, guided practice and assessment. Work moves from small examples to a bounded engineering task. Contact hours describe this allocation; independent study and additional bridge preparation may sit outside it.
03 / Build and test a project
Projects are designed around observable behavior: a controller reacts to inputs, a perception pipeline processes a scene, or an agent completes a bounded workflow. Test normal operation and failure cases. Preserve configurations, commands, data assumptions and results so the work can be reproduced.
04 / Explain your evidence
Assessment combines project artifacts and individual explanation. Be ready to explain design choices, test results, limitations and what you would change. A successful simulation and a physical-platform demonstration are different forms of evidence and must be described accurately.
Practical access is course-specific
Simulation, recorded datasets and physical equipment serve different learning goals. Ask for the exact delivery plan, equipment access, software licences, compute allocation and assessment arrangements for an intake before enrolling.