๐๏ธ Week 06 - Bayesian Classification
After two weeks of exploring the foundations of approximate Bayesian inference, we are now ready to dive into the world of Bayesian classification. We will discuss how to apply the concepts we have learned so far to classification problems, starting from binary classification. We will discuss the logistic regression model and how it can be interpreted as a Bayesian model. Using the tools we have developed, we will show how to actually perform inference with this model. We will also explore the concept of posterior predictive checks and how they can be used to evaluate the performance of our models. Finally, we will talked about another approach to classification, the Naive Bayes classifier.
๐ Lecture Slides
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๐ Recommended Reading
- Murphy (2023): Chapter 15.3 (Logistic Regression)
๐ References
Murphy, Kevin P. 2023. Probabilistic Machine Learning: Advanced Topics. MIT Press. http://probml.github.io/book2.