Mathematics of machine learning

Your learning path

1. 01

   Linear regression, residuals and squared loss

   A linear model predicts yhat=w·x+b. Linear refers to the parameters; feature transformations may make the relation nonlinear in original inputs.

   University · Notes · 8 questions
2. 02

   Gradient descent and learning-rate choices

   Gradient descent uses θnew=θ−η∇L(θ) with a positive step size η. The negative gradient is locally downhill for a differentiable objective.

   University · Notes · 8 questions
3. 03

   Logistic scores, thresholds and confusion matrices

   A logistic model maps a real score through σ(z)=1/(1+exp(−z)). Interpreting a score as a reliable probability requires calibration assessment.

   University · Notes · 8 questions
4. 04

   Overfitting, regularization and data distribution

   A model can fit training-specific noise or patterns that do not generalize. Compare training and appropriately held-out performance.

   University · Notes · 8 questions