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.
Your route
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- What is a signal?What does a waveform actually tell you?
- Frequency, amplitude and phaseHow can three numbers describe an oscillation?
- Vectors and similarityHow does a list of numbers become a geometric object?
- Complex numbers and rotationWhy do transforms use an imaginary coordinate?
- Slopes and gradientsHow do you decide which way to change a parameter?
- Probability and uncertaintyHow should several plausible outcomes share one unit of probability?
- Data splits and leakageWhen is a good test score too good to be true?
- Sampling and timeHow do equally spaced measurements encode motion?
- Projection onto wavesHow can a dot product detect a frequency?
- The discrete Fourier transformWhat exactly is in an FFT output array?
- Windows and leakageWhy does one tone make many spectral bars?
- Linearity and time invarianceWhen can one impulse response describe a whole system?
- Convolution step by stepWhich input samples contribute to one output?
- Features and linear regressionWhat exactly does a model learn from examples?
- Gradient descentHow can a repeated local update find better parameters?
- Classification and cross-entropyWhy penalise a confidently wrong prediction so strongly?
- Evaluation and generalisationWhich mistakes does a single accuracy score hide?
- Nonlinear features and neural networksWhat does adding a hidden layer actually add?
- The chain rule and backpropagationHow does one weight receive credit for a distant error?
- Convolutions in neural networksHow does one learned filter scan an entire representation?
- Regularisation and model selectionHow can a better training fit make a worse model?
- Numerical stabilityCan the same exact formula behave differently on a machine?
- Project: a leakage-safe classifierCan your feature choice survive a truly held-out evaluation?
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