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Applied Probabilistic Programming & Bayesian Machine Learning (MIT IAP 2017)

Project README


This repository is a collection of material designed for a 2017 IAP class at MIT titled "Applied Probabilistic Programming & Bayesian Machine Learning." It includes:

  • Lecture notes (in pdf format)
  • Python, R, and Stan coding exercises
  • Toy Datasets (csv format)

The material is largely a "remixing" of two textbooks: Statistical Rethinking, and Bayesian Data Analysis 3.


Powerpoint slides, which discuss topics both included and not included in the course material, are available here: https://www.dropbox.com/sh/zph738h4a0x4a1v/AACkZaOHxqjnDAjhq4s-hFala?dl=0


Required Software:

  • Git (optional)
  • Python 2.7*
  • pystan
  • R
  • RStan.

If you have Git, you can clone this repository. If you do not, you can download this repository as a compressed folder. Git installation guide: https://git-scm.com/book/en/v2/Getting-Started-Installing-Git

We recommend Anaconda (https://www.continuum.io/downloads) as an OS-agnostic solution to installing Python and pystan without administrative privileges. For Windows users, Anaconda also provides a GUI. Once you have anaconda, you can install pystan without root by using:

> conda install -c anaconda pystan

If you're on Windows, see http://pystan.readthedocs.io/en/latest/windows.html for installating Pystan.

To install R without root, see here: http://unix.stackexchange.com/posts/149455

To install RStan for Linux/Mac: https://github.com/stan-dev/rstan/wiki/Installing-RStan-on-Mac-or-Linux

To install RStan for Windows: https://github.com/stan-dev/rstan/wiki/Installing-RStan-on-Windows

* Note: Python code is written for 2.7, but is easy to convert to Python 3 compatible code if needed.

Open Source Agenda is not affiliated with "Iap Appbml" Project. README Source: maxwshen/iap-appbml
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