Learning library

Make the foundations concrete.

Build working knowledge of robotics and machine learning through examples you can change, calculations you can check, and data you can inspect.

Start with the map of machine learning fields to connect tasks, data, learning signals, and model choices.

Search lesson titles, concepts, and the explanations themselves.

Explore topics

Choose any topics, then search. Results can match any selected topic.

99 lessons to explore

Foundations first

  1. Foundations / 9 min read

    Vector spaces: build direction from addition and scaling

    Learn vector spaces through robot displacement. Explore span, linear independence, basis, and dimension with an interactive diagram, Python, and exercises.

  2. Foundations / 9 min read

    The dot product: angles, projections, and robot motion

    Learn the dot product with an interactive vector diagram, a worked projection example, and a robot heading calculation. Includes exercises and solutions.

  3. Foundations / 8 min read

    Conditional probability: reading a robot sensor

    Learn conditional probability with an interactive robot sensor example. Work through the formula, Bayes' rule, base rates, and common mistakes using counts.

  4. Foundations / 8 min read

    Gradient descent: learn the update, then test its limits

    Work through gradient descent by hand, then explore learning rates, overshoot, and stationary points in an interactive experiment with Python and exercises.

  5. Foundations / 9 min read

    Linear regression: fit a line and inspect its errors

    Fit linear regression to robot sensor readings. Work through least squares, residuals, and MSE, then test an outlier with Python and exercises.

  6. Foundations / 8 min read

    Bayesian inference: learning a robot's grasp success rate

    Learn Bayesian inference through a robot grasp example. Update a Beta prior with successes and failures, then distinguish uncertainty from prediction.

  7. Foundations / 9 min read

    Eigenvalues and eigenvectors: find the lines a matrix preserves

    Understand eigenvalues through 2D transformations. Test stretching, reversal, zero eigenvalues, rotation, and repeated values, then connect them to robot error dynamics.

  8. Foundations / 10 min read

    Tensors: read shapes, select values, and move axes

    Learn tensors through a robot image batch. Explore shape, indexing, slicing, and axis order, then compare reshape with transpose using a runnable Python example.

  9. Foundations / 10 min read

    Logistic regression: turn a score into a probability

    Explore logistic regression with synthetic robot observations. Calculate sigmoid probabilities and log loss, then change a threshold and inspect the confusion matrix.

  10. Foundations / 9 min read

    Markov chains: transitions and stationary distributions

    Learn Markov chains with a robot example. Explore transition matrices, stationary distributions, periodic chains, and absorbing states with an interactive model.

  11. Foundations / 9 min read

    Laplace distribution: model sensor error and tolerance

    Learn the Laplace distribution through sensor error. Explore location, scale, density, interval probabilities, and the link between absolute error and median fitting.

  12. Foundations / 11 min read

    Random forests: train different trees and combine their votes

    Build a small random forest from synthetic robot observations. Inspect learned splits, bootstrap samples, random feature choices, and individual tree votes.