Week 4: Unsupervised Learning and Probability Review

Lecture Notes

Unsupervised Learning and Probabilities

Learning Outcomes Week 4

In this lecture, the following concepts were introduced.

• The difference between unsupervised learning and supervised learning.
• Examples of unsupervised learning approaches:
  • Clustering: e.g. k-means clustering
  • Dimensionality reduction: e.g. principal component analysis.
• The algorithms for dimensionality reduction and clustering involve optimisation of objective functions.
• The different characteristics of these approaches to dimensionality reduction: in clustering you represent your data as discrete groups, in dimensionality reduction by a reduced number of continuous variables.
• Understand that machine learning has two broad approaches
  • The Optimization Approach to ML
  • The Probabilistic Approach to ML
  And that these approaches are related: often the error function has a probabilistic interpretation through being the negative log likelihood.
• The basic probability rules including:
  • The properties of a probability distribution.
  • The sum rule of probability.
  • The product rule of probability.
  • Bayes' rule
• How these rules are applied in a simple robot navigation example.
• The difference between a machine learning model and a machine learning algorithm.

This document last modified Wednesday, 06-Nov-2013 08:42:26 UTC