A guided journey · 23 lessons

Machine learning

From vectors and gradients to neural networks and an honestly evaluated signal classifier.

Prerequisites are included automatically. Suggested lesson time: 11 hours, plus extra time for experimentation and projects. This is an estimate, not a measured completion time.

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  1. What is a signal?What does a waveform actually tell you?15 min · Progress unavailable without JavaScript
  2. Frequency, amplitude and phaseHow can three numbers describe an oscillation?20 min · Progress unavailable without JavaScript
  3. Vectors and similarityHow does a list of numbers become a geometric object?20 min · Progress unavailable without JavaScript
  4. Complex numbers and rotationWhy do transforms use an imaginary coordinate?20 min · Progress unavailable without JavaScript
  5. Slopes and gradientsHow do you decide which way to change a parameter?25 min · Progress unavailable without JavaScript
  6. Probability and uncertaintyHow should several plausible outcomes share one unit of probability?20 min · Progress unavailable without JavaScript
  7. Data splits and leakageWhen is a good test score too good to be true?20 min · Progress unavailable without JavaScript
  8. Sampling and timeHow do equally spaced measurements encode motion?20 min · Progress unavailable without JavaScript
  9. Projection onto wavesHow can a dot product detect a frequency?25 min · Progress unavailable without JavaScript
  10. The discrete Fourier transformWhat exactly is in an FFT output array?30 min · Progress unavailable without JavaScript
  11. Windows and leakageWhy does one tone make many spectral bars?30 min · Progress unavailable without JavaScript
  12. Linearity and time invarianceWhen can one impulse response describe a whole system?25 min · Progress unavailable without JavaScript
  13. Convolution step by stepWhich input samples contribute to one output?30 min · Progress unavailable without JavaScript
  14. Features and linear regressionWhat exactly does a model learn from examples?30 min · Progress unavailable without JavaScript
  15. Gradient descentHow can a repeated local update find better parameters?30 min · Progress unavailable without JavaScript
  16. Classification and cross-entropyWhy penalise a confidently wrong prediction so strongly?30 min · Progress unavailable without JavaScript
  17. Evaluation and generalisationWhich mistakes does a single accuracy score hide?30 min · Progress unavailable without JavaScript
  18. Nonlinear features and neural networksWhat does adding a hidden layer actually add?30 min · Progress unavailable without JavaScript
  19. The chain rule and backpropagationHow does one weight receive credit for a distant error?35 min · Progress unavailable without JavaScript
  20. Convolutions in neural networksHow does one learned filter scan an entire representation?30 min · Progress unavailable without JavaScript
  21. Regularisation and model selectionHow can a better training fit make a worse model?30 min · Progress unavailable without JavaScript
  22. Numerical stabilityCan the same exact formula behave differently on a machine?35 min · Progress unavailable without JavaScript
  23. Project: a leakage-safe classifierCan your feature choice survive a truly held-out evaluation?90 min · Progress unavailable without JavaScript

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