Module 1: Unsupervised machine learning

Introduction

Unsupervised Learning

• A framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data.

• Other frameworks include weak- or semi-supervision, and a small portion are considered self-supervision (but many scientists consider this unsupervised learning).

Why Unsupervised Learning?

• Discover hidden structures or data groupings

• Ideal for exploratory data analysis

Clustering

• Data mining technique used to form groupings

Factor Analysis

• Dimension reduction when we have lots of variables

Q-Matrix

• Knowledge inference

• Skill-item mapping or knowledge component (KC) models

This week

• We will go in depth on clustering, factor analysis, and Q-matrix methods

• How to use them to discover insights from data

• How to avoid obtaining meaningless findings

• We will cover examples of each of these three forms of unsupervised learning

What applications are you interested in?

• Who here has already used clustering, factor analysis, or Q-Matrix (or something like it)?

What applications are you interested in?

• Who here has already used clustering, factor analysis, or Q-Matrix (or something like it)?

• Tell us more – about the data, about the goal of your analysis

First Up

Clustering