Readings "Passive" learning opportunities

This page collects together all of the “readings” associated with individual modules.

In this site, readings represent “passive” learning opportunities, as opposed to experiences, which represent “active” learning opportunities. In many courses, readings and experiences together constitute the “assignments”.

Module: Introduction to Machine Learning

EE 445 Syllabus

Basic information about the class

Module: Learning conjunctions in a PAC framework

Learning conjunctions

Notes from class

PAC Learning

Book

Module: Gaussians

Handout: Univariate and Multivariate Gaussians

Univariate and Multivariate Gaussians, conditional expectation

Module: Linear Regression

Ordinary Least Squares/Maximum Likelihood

Class notes

Bayesian Formulation/Ridge Regression

Class notes

Geometry of Linear Regression

Class notes

Generalization and Significance: classical perspective

Class notes

Generalization and Significance

Class notes

Module: Single neuron networks

Perceptrons

Perceptron Learning

Module: Support vector machines

Setup: Linearly separable case

Setup: Linear Case

Dual formulation: Representer Theorem

Setup: Linear Case

Module: Linear Classifiers

Fisher Discriminant

Lin Reg with categorical labels interpreted as numbers

Max Entropy: Logistic Regression

Max Entropy approach to modeling classes

Max margin: Support Vector Machine

Max margin approach: Support Vector Machines