Programming

• Python fluency for data handling and numerical work
• Introducing scikit-learn with genuine datasets and honest evaluation
• Version control and reproducible student project practice

Methods

• Training, validation and testing taught as a discipline, not a formality
• Overfitting demonstrated experimentally rather than asserted
• Neural network fundamentals and a first hands-on training run

Robotics

• Sensor fusion, control loops and feedback on real hardware
• Computer vision on constrained devices
• Integrating a trained model into a robot behaviour

Guidance

• Supervising research-style capstone projects and scoping them realistically
• Ethics discussion at a level that withstands senior-student scrutiny
• Advising on university pathways and portfolio building