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.