- matrix calculus
- supervised learning
- gradient descent
- Newton’s Method
- regression
- \(y|x,\theta\) is linear Linear Regression
- What if \(X\) and \(y\) are not linearly related? Generalized Linear Model
- \(y|x, \theta\) can be any distribution that’s exponential family
- some exponential family distributinos: SU-CS229 Distribution Sheet
- classification
- take linear regression, squish it: logistic regression; for multi-class, use softmax \(p\qty(y=k|x) = \frac{\exp \theta_{k}^{T} x}{\sum_{j}^{} \exp \theta_{j}^{T} x}\)
- generative learning
- modeling each class’ distributions, and then check which one is more likely: GDA
- Naive Bayes
- bias variance tradeoff
- regularization
- unsupervised learning
- feature map and precomputing Kernel Trick
- Decision Tree
