"Probabilistic Machine Learning: An Introduction"

Chapters

Chapter	Name	Notebooks
1	Introduction	01/
2	Probability: univariate models	02/
3	Probability: multivariate models	03/
4	Statistics	04/
5	Decision theory	05/
6	Information theory	06/
7	Linear algebra	07/
8	Optimization	08/
9	Linear discriminant analysis	09/
10	Logistic regression	10/
11	Linear regression	11/
12	Generalized linear models	12/
13	Neural networks for unstructured data	13/
14	Neural networks for images	14/
15	Neural networks for sequences	15/
16	Exemplar-based methods	16/
17	Kernel methods	17/
18	Trees	18/
19	Learning with fewer labeled examples	19/
20	Dimensionality reduction	20/
21	Clustering	21/
22	Recommender systems	22/
23	Graph embeddings	23/