Implementing Machine Learning Algorithms
This week we continued with the
updated lecture slides from week 4, with a particular focus on continuous probability densities, and how they represent distributions, and then an illustration of how maximum likelihood and minimum error are the same for a Gaussian density and least squares error function.
Learning Outcomes Week 5
This lecture covers the following learning outcomes
- A review of continuous probability densities.
- A review of the Gaussian density.
- The equivalence between least squares and a Gaussian noise approximation.