chapter_no_to_name_mapping_book2.csv

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