Probabilistic Machine Learning: Advanced Topics

Chapters

chap_no	chap_name	Notebook
1	Introduction	01/
2	Probability	02/
3	Bayesian statistics	03/
4	Probabilistic graphical models	04/
5	Information theory	05/
6	Inference algorithms: an overview	inf/
7	Optimization	opt/
8	Inference for state-space models	08/
9	Inference for graphical models	09/
10	Variational inference	10/
11	Monte Carlo inference	11/
12	Markov Chain Monte Carlo inference	12/
13	Sequential Monte Carlo inference	13/
14	Predictive models: an overview	14/
15	Generalized linear models	15/
16	Deep neural networks	16/
17	Bayesian neural networks	17/
18	Gaussian processes	18/
19	Beyond the iid assumption	19/
20	Generative models: an overview	20/
21	Variational autoencoders	21/
22	Auto-regressive models	22/
23	Normalizing Flows	23/
24	Energy-based models	24/
25	Diffusion models	25/
26	Generative adversarial networks	26/
27	Discovery methods: an overview	27/
28	Latent factor models	28/
29	State-space models	29/
30	Graph learning	30/
31	Non-parametric Bayesian models	31/
32	Representation learning	32/
33	Interpretability	33/
34	Decision making under uncertainty	34/
35	Reinforcement learning	35/
36	Causality	36/