Bayesian Learning using Python
Abinash Panda (~abinashpanda) 
31
Description:
Introduction
 The aim of this workshop is to introduce users to the Bayesian approach of statistical modeling and analysis.
 In this workshop we would be covering
 Markov Chain Monte Carlo (MCMC). These methods are a class of algorithms for sampling from a probability distribution based on constructing a Markov chain.
 Probabilistic Graphical Models (PGMs). It is a technique of machine learning which uses a network structure of random variables and smaller conditional probability distributions to represent the Joint distribution over all the variables. Inference/prediction can be performed over these models by conditioning it over the given values and computing the conditional probability distribution.
 This talk mainly focuses on
 usage of
PyMC
for MCMC  classification problems using Bayesian Models and how to work with them using
pgmpy
.
 usage of
Tutorial

Introduction to SciPy stack
 Introduction to numpy
 Brief introduction to scipy
 Visualization using matplotlib

Introduction to Probability Theory
 Basics of probability theory with handson excercise
 Bayesianist vs Frequentist

Thinking like Bayesianist
 Bayes Theorem and handson excercise
 Introduction to Markov Chain Monte Carlo (MCMC)
 MCMC using PyMC

Probabilistic Graphical Models (PGMs)
 Introduction to PGMs
 PGMs using pgmpy
 Creating models using pgmpy
 Parameterizing the model
 Asking questions to the model: Inference
 Special graphical models
 Naive Bayes Models
 Hidden Markov Models
Take home Bonus
 How to compute optimal parameters for the models
 How to construct the network structure from the data if we don't have any domain knowledge.
Prerequisites:
 Basic knowledge of Python
 Knowledge of SciPy stack would be an added bonus (optional).
Content URLs:
 Initial draft of the presentation: http://nbviewer.ipython.org/github/pgmpy/pgmpy_notebook/blob/master/Probabilistic%20Graphical%20Models%20using%20pgmpy.ipynb
 PyMC: https://pymcdevs.github.io/pymc/
 Pgmpy: http://pgmpy.org
 Numpy: http://www.numpy.org/
 Scipy: http://docs.scipy.org/doc/scipy/reference/
 Matplotlib: http://matplotlib.org/index.html
Speaker Info:
Abinash Panda is an undergraduate from IIT (BHU) Varanasi and is currently working as a Data Scientist. He has been a contributor to opensource libraries such as Shogun Machine Learning Toolbox and pgmpy which he started writing along with four other members. He is spending most of his free time in improving pgmpy and helping new contributors.
Ankur Ankan is a B.Tech graduate from IIT Varanasi who is currently working in the field of data science. He is an open source enthusiast and his major work includes starting pgmpy with four other members. Presently he is working on improving the performance of pgmpy and also mentoring GSoC students participating under pgmpy. In his free time he likes to participate in Kaggle competitions.
Speaker Links:

github:
 https://github.com/abinashpanda
 https://github.com/ankurankan

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