# 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.

- Clustering: e.g.
- 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.
- Bayesâ€™ rule

- How these rules are applied in a simple robot navigation example.
- The difference between a machine learning
*model*and a machine learning*algorithm*.