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aiguideReading NTU Hsuan-Tien Lin Machine Learning Foundations & Techniques

Types of Learning: Output Space, Labels, Protocol, and Input Space | NTU Hsuan-Tien Lin Machine Learning Foundations Lecture 3

This lecture takes the learning flow diagram from Lecture 1 and swaps one part at a time along four directions: the output space Y (binary classification, multiclass classification, regression, structured learning), the labels yₙ (supervised, unsupervised, semi-supervised, reinforcement learning), the way data reaches the algorithm (batch, online, active), and the input space X (concrete, raw, abstract features). Each direction has one core option, and together they mark the home ground for the rest of Foundations: batch, supervised binary classification and regression on concrete features.

#ntu-htlin-ml#ntu#ai-course#machine-learning#supervised-learning#reinforcement-learning#feature-engineering
aiguideReading NTU Hsuan-Tien Lin Machine Learning Foundations & Techniques

Nonlinear Transformation: The Price of Curved Boundaries | NTU Hsuan-Tien Lin Machine Learning Foundations Lecture 12

When no straight line separates the data, you do not need to redesign the algorithm: use a feature transform Φ to turn x into z, run a linear model in Z-space as usual, and the line in Z-space is a quadratic or higher-order curve in X-space. The Q-th order polynomial transform raises the dimension to d̃ = C(Q+d, d) − 1, and computation, storage, and the VC dimension (at most d̃+1) all grow with it, so the lecture concludes that you should try the linear model first and move to higher orders only when it is not enough.

#ntu-htlin-ml#ntu#ai-course#machine-learning#feature-engineering#classification#learning-theory
aideep-diveReading CMU 07-280 AI & ML I

Trading Expressiveness for Stability with Features and Regularization | CMU 07-280 Lecture 10

Lecture 10 uses φ(x) to let linear models express nonlinear functions, then controls the resulting overfitting with train/validation/test separation, L1/L2 regularization, and model selection.

#cmu#ai-course#machine-learning#feature-engineering#regularization