4. Supervised Learning (and some unsupervised)
2021: this page has now been updated and is ready to go
Learning Goals: You should have heard about support vector machines. You should understand Bayesian learning and be able to use Bayes' formula. You should understand the ideas of Gaussian Mixture Models and how it is used in the LDA and QDA algorithms. You should be able to describe how one can learn from unlabeled data and how the EM and k-means algorithms work. You should be able to explain the singular value decomposition (SVD) and how it relates to principal component analysis (PCA).
You find the Lecture 4 slides here
Download Lecture 4 slides here
You find the Lecture 4 video here
or on Youtube here: https://youtu.be/C2nhmFWbNAY Links to an external site.
Code used on lecture
pcaMNIST.ipynb (Links to an external site.)
kmeans.ipynb (Links to an external site.)
mixedmodel.m Download mixedmodel.m
mixedmodelunsupervised.m Download mixedmodelunsupervised.m
pcacancer.m Download pcacancer.m
Some additional video
What are A priori and A posteriori - Gentleman Thinker (Links to an external site.)