Alexander Ihler

Clustering (4): Gaussian Mixture Models and EM

Clustering (3): K-Means Clustering

Clustering (2): Hierarchical Agglomerative Clustering

Clustering (1): Basics

Neural Networks (2): Backpropagation

Neural Networks (1): Basics

Support Vector Machines (3): Kernels

Support Vector Machines (2): Dual & soft-margin forms

Support Vector Machines (1): Linear SVMs, primal form

VC Dimension

Linear classifiers (2): Learning parameters

Linear classifiers (1): Basics

Linear regression (6): Regularization

Linear regression (2): Gradient descent

Linear regression (4): Nonlinear features

Linear regression (5): Bias and variance

Linear regression (3): Normal equations

Linear regression (1): Basics

Introduction (2): Data and Visualization

Introduction (1): AI & Machine Learning

Introduction (3): Supervised Learning

Introduction (4): Complexity and Overfitting

Bayes Classifiers (2): Naive Bayes

Bayes Classifiers (1)

Nearest Neighbor (1)

Nearest neighbor (2): k-nearest neighbor

Multivariate Gaussian distributions

PCA, SVD

Clustering

Review: Probability