CS156a

Caltech Machine Learning Course - Yaser

Set 1: Perceptron Learning Algorithm

Here, I created a Python script to implement the perceptron learning algorithm on a random 2-d function over the space [-1, 1] X [-1, 1].

Set 2: Linear Regression and Nonlinear Transforms

Set 3: Growth Functions and Break Points

Set 4: Generalization Error, VC Dimension, and Bias & Variance

Set 5: Gradient Descent and Logistic Regression

Set 6: Overfitting, Stochastic and Deterministic Noise, Regularization, and Neural Networks

Set 7: Validation Bias, Cross Validation, PLA vs SVM

Set 8: Primal vs Dual Problem, SVM with soft margins, Polynomial Kernels, RBF Kernels, Cross Validation