ML Fundamentals
Bias/variance, regression, classification — the basics.
mediumLearning
Why it matters in robotics
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Application focus
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At a glance
The supervised learning loop: fit a model on training data, predict on held-out data, evaluate, then diagnose bias vs. variance to tune complexity.
What to study
- ✓Bias-variance tradeoff, overfitting/underfitting, and train/validation/test splits with cross-validation
- ✓Linear and logistic regression: cost functions, gradient descent, and decision boundaries
- ✓Regularization (L1/L2) and feature scaling to control model complexity
- ✓Evaluation metrics: MSE/R² for regression, accuracy/precision/recall/confusion matrix for classification
Study by time budget
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