Machine Learning Basics
Coming Soon
This lesson is currently under development. Check back soon for comprehensive content covering:
- Learning Paradigms: Supervised, unsupervised, semi-supervised, reinforcement learning
- Training vs. Testing: Generalization, overfitting, cross-validation
- Evaluation Metrics: Accuracy, precision, recall, F1-score, confusion matrices
- Robotics Applications: Perception model training, behavior learning, anomaly detection
- Practical Considerations: Data collection for robots, domain shift, active learning
Expected Completion: This lesson will be available soon.
Learning Objectives
By the end of this lesson, you will be able to:
- Distinguish between learning paradigms and select appropriate approaches for robotics tasks
- Evaluate model performance using appropriate metrics for robot perception and control
- Diagnose overfitting and apply regularization techniques
- Understand data requirements for training robot learning systems
Further Reading
- Hands-On Machine Learning by Aurélien Géron
- Robot Learning — TU Berlin robotics course
- Scikit-learn Documentation — ML fundamentals with Python
What's Next?
Continue to Lesson 2: Neural Networks Refresher to dive into deep learning architectures.
This lesson is part of Chapter 2: AI Fundamentals Review