Overview
What this challenge is about.
Train a CNN for lane-following on a Donkeycar simulator, run ablations, and create a teaching notebook. Finish with a verifiable certificate.
The scenario
The robotics lab teaches around 40 graduate students per cohort; a clean teaching reference saves about 200 staff-hours of TA debugging support per semester.
The Brief
What you'll do, and what you'll demonstrate.
Ship a reproducible end-to-end lane-following teaching reference with 3 ablations and a student-ready lab guide.
Earning criteria — what you'll demonstrate
- Train an end-to-end imitation-learning policy from camera data
- Design and run informative ablations
- Evaluate driving models in simulation with clear metrics
- Document a teaching reference that students can rerun
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- End To End Learning
Apply end to end learning to solve real industry problems and demonstrate production-level capability.
- Imitation Learning
Apply imitation learning to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Data Augmentation
Apply data augmentation to solve real industry problems and demonstrate production-level capability.
- Ablation Study
Apply ablation study to solve real industry problems and demonstrate production-level capability.
- Documentation
Apply documentation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Packaging an end-to-end ML system as a reproducible reference is the AI-engineer skillset that scales teams and onboards new colleagues.
This challenge sharpens
- end-to-end-learning
- ablation-study
- documentation
Machine Learning Engineer
Designing informative ablations with proper hold-out evaluation is the MLE habit that distinguishes shippable from one-shot work.
This challenge sharpens
- imitation-learning
- pytorch
- ablation-study
AI Product Designer
Writing a lab guide a first-week student can follow is the UX-of-ML craft AI product designers carry into product surfaces.
This challenge sharpens
- documentation
- data-augmentation
- ablation-study