Alexander Ihler

Decision Trees (1)

Linear regression (2): Gradient descent

Nearest Neighbor (1)

Linear regression (1): Basics

Neural Networks (1): Basics

Review: Probability

VC Dimension

Ensembles (1): Basics

Ensembles (4): AdaBoost

Clustering (4): Gaussian Mixture Models and EM

Clustering (2): Hierarchical Agglomerative Clustering

Linear regression (6): Regularization

Bayes Classifiers (1)

Ensembles (3): Gradient Boosting

Clustering (3): K-Means Clustering

Linear classifiers (1): Basics

Introduction (4): Complexity and Overfitting

Introduction (3): Supervised Learning

Neural Networks (2): Backpropagation

Linear regression (4): Nonlinear features

Linear regression (5): Bias and variance

Bayes Classifiers (2): Naive Bayes

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

Introduction (2): Data and Visualization

Linear classifiers (2): Learning parameters

Linear regression (3): Normal equations

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

Support Vector Machines (3): Kernels

Introduction (1): AI & Machine Learning