starts edits for Version 2

01_intro.md

@@ -1,12 +1,12 @@
-# Bayesian Modelling and Probabilistic Programming with Numpyro and examples from Epidemiology.
+# Bayesian modelling with Numpyro and deep generative surrogates for epidemiology.
 
-Welcome to the course! The course materials are a WORK IN PROGRESS. For the latest version, as the PDF may not render everything correctly, visit <https://elizavetasemenova.github.io/prob-epi/>.
+Welcome to the course! The course materials are a WORK IN PROGRESS. If you are using the PDF, please refer to the online content at <https://elizavetasemenova.github.io/prob-epi/> for the latest updates, as the PDF may not render everything accurately.
 
 ## About the author
 
-These lecture notes were written by Elizaveta (a.k.a. Liza) Semenova. I am a lecturer in Biostatistics, Computational Epidemiology and Machine Learning at Imperial College London. My work is centered around scalable and flexible methods for spatiotemporal statistics and Bayesian machine learning with applications in epidemiology. This course is meant to set you up well for doing similar research. 
+These lecture notes were written by <span style="color:orange">Elizaveta Semenova</span> (a.k.a. Liza). I am a lecturer in Biostatistics, Computational Epidemiology and Machine Learning at Imperial College London. My work is centered around scalable and flexible methods for spatiotemporal statistics and Bayesian machine learning using probabilsitic programming with applications in epidemiology. This course is meant to set you up well for doing similar research. 
 
-Most recently, my focus has been on using deep generative modelling to power MCMC inference in classical spatial statistics, as well as adaptive survey design. Even though this course does not touch these subjects, feel free to reach out to discuss. 
+Most recently, my focus has been on using deep generative models to power MCMC inference in classical spatial statistics. It turns out that the same method works for a much wider range of applications, including disease transmission modelling! In part, this course does touch on these subjects. Feel free to reach out to discuss the landscape. 
 
 More details about my work are available [here](https://www.elizaveta-semenova.com/).
 
@@ -27,12 +27,14 @@ Acknowledging here that learning does not always have to be enjoyable.
 - If you are creating a written document (a paper, report, book chapter) where you use what you've learnt here, please cite
 
 ```
-@book{semenova24,
-  author = {Semenova, Elizaveta},
-  title = {Bayesian Modelling and Probabilistic Programming with Numpyro and examples from Epidemiology.},
-  year = {2024},
-  source = {https://elizavetasemenova.github.io/prob-epi},
-  doi = {https://doi.org/10.5281/zenodo.11550659}
+@software{Semenova_Bayesian_Modelling_and_2024,
+author = {Semenova, Elizaveta},
+doi = {10.5281/zenodo.11550659},
+month = jun,
+title = {{Bayesian Modelling and Probabilistic Programming with Numpyro and examples from Epidemiology.}},
+url = {https://github.com/elizavetasemenova/prob-epi},
+version = {v1.0.0},
+year = {2024}
 }
 ```
 

02_about.md

@@ -1,14 +1,26 @@
 # About this course
 
-This online book consists of lecture notes of the course which will I taught during three weeks from 25 March to 12 April 2024 to the inaugural MSc ["AI for Science"](https://ai.aims.ac.za/) cohort at the [African Institute for Mathematical Sciences (AIMS)](https://aims.ac.za/), South Africa. 
+This online book consists of lecture notes of the course which I taught during 
 
-## Content
+-  25 March to 12 April 2024 to the inaugural cohort, 
+- 25 November to 13 December 2024 to the second cohort
 
-In this course we will cover such topics as Bayesian inference, hierarchical modelling, Gaussian processes for spatial statistics, ordinary differential equations for disease transmission modelling. 
+of MSc ["AI for Science"](https://ai.aims.ac.za/) at the [African Institute for Mathematical Sciences (AIMS)](https://aims.ac.za/), South Africa. 
 
-We will build probabilistic models and perform inference using a probabilistic programming language `Numpyro`  in a fully Bayesian manner to characterise uncertainty of the modelled quantities.
 
-Although the course is primarily <span style="color:orange">computational</span> in nature, the models which we will examine are inspired by the typical modelling practices found in epidemiology.
+The title of the course for the first cohort was "Bayesian Modelling and Probabilistic Programming with Numpyro and examples from Epidemiology''.
+
+The title of the course for the second cohort is "Bayesian Modelling with Numpyro and Deep Generative Surrogates for Epidemiology''.
+
+## Abstract
+
+In this course we will explore a range of topics in Bayesian modelling, such as Bayesian inference, hierarchical modelling, Gaussian processes for spatial statistics, ordinary differential equations and agent-based models for disease transmission modelling. 
+
+Using the probabilistic programming language `Numpyro`, we will construct probabilistic models and perform Bayesian inference to quantify uncertainty in model predictions and parameter estimates. 
+
+As the course progresses, we will introduce <span style="color:orange">deep generative models</span> as efficient surrogates for computationally demanding model components (yes, this is 'generative AI'!). These surrogates, implemented in `JAX`, will be integrated seamlessly into Numpyro programs, enabling fast and scalable MCMC inference. 
+
+While the course emphasises computational techniques, the models and applications are rooted in real-world epidemiology, providing a practical framework for data-driven decision-making in health research.
 
 
 ## Prerequisites