Uploaded February 2019 | Updated September 2026, 3 days ago
slides: mlvu.github.io/lectures/31.ProbabilisticModels1.annotated.pdf
course materials: mlvu.github.io
Apologies for the bad audio (and missing video). A technical mishap meant we had to improvise.
Today, we discuss probabilistic approaches to Machine Learning. We start by reviewing the basics of probability theory and information theory, and then discuss some approaches to probabilistic classification, including the (naive) Bayes classifier and logistic regression, which is a combination of a linear model, a sigmoid function and cross-entropy loss.
slides: mlvu.github.io/lectures/31.ProbabilisticModels1.annotated.pdf
course materials: mlvu.github.io
Apologies for the bad audio (and missing video). A technical mishap meant we had to improvise.
Today, we discuss probabilistic approaches to Machine Learning. We start by reviewing the basics of probability theory and information theory, and then discuss some approaches to probabilistic classification, including the (naive) Bayes classifier and logistic regression, which is a combination of a linear model, a sigmoid function and cross-entropy loss.
