Feb 8, 2021 | 8 min read
Bayesian Inference for the Poisson Model
This lecture discusses Bayesian inference for the Poisson model, including conjugate prior specification, a different way to specify a "non-informative" prior, and relevant posterior summaries.
Feb 3, 2021 | 7 min read
We talk about one of the simplest classification methods, naive Bayes classifiers, and its applications in text classification. It's not really machine learning as we only need a single pass through the data to compute necessary values.
Nov 18, 2020 | 20 min read
Eigenvalues and Eigenvectors
Probably the most important lecture in this course -- we start from the calculation of eigenvalues and eigenvectors, and move on to related topics such as the eigendecomposition, singular value decomposition, and the Moore-Penrose inverse.