Introduction to Bayesian Networks(3 hour workshop)
Most machine learning models assume independent and identically distributed (i.i.d)
data. Graphical models can capture almost arbitrarily rich dependency structures
between variables. They encode conditional independence structure with graphs.
Bayesian network, a type of graphical model describes a probability distribution
among all variables by putting edges between the variable nodes, wherein edges
represent the conditional probability factor in the factorized probability distribution.
Thus Bayesian Networks provide a compact representation for dealing with
uncertainty using an underlying graphical structure and the probability theory. These
models have a variety of applications such as medical diagnosis, bio monitoring,
image processing, turbo codes, information retrieval, document classification, gene
regulatory networks, etc. amongst many others. These models are interpretable as
they are able to capture the causal relationships between different features .They
can work efficiently with small data and also deal with missing data which gives it
more power than conventional machine learning and deep learning models.
In this session, we will discuss concepts of conditional independence, d- separation,
Hamersley Clifford theorem, Bayes theorem, Expectation Maximization and Variable
Elimination. There will be a code walk through of simple case study.
Detailed Breakdown of the workshop
2. Bayesian Networks
3. Independence in Bayesian Networks (covers d separation, Hamersley Clifford)
4. Inference (covers Variable Elimination)
5. Missing data (Expectation Maximization)
6. Case Study using Bayesian networks(Handson using pgmpy package)
Abinash is the Co -Founder of a startup -Prodios and has been a data scientist for more than 4 years. He has worked in multiple early stage startups and helped them build their data analytics pipeline. He love to munge, plot and analyse data. He has been a speaker at several Python conferences.
Abinash Panda has written two books in Probabilistic Graphical Models and HMM
He is the founding member and significantly contributed to pgmpy package.
I am a polymath and unicorn data scientist with strong foundations in Economics, Finance, Business Foundations, Business Analytics and Psychology. I specialize in Probabilistic Graphical Models, Machine Learning and Deep Learning. I have completed Financial Engineering and Risk Management program from Columbia University with top honors, micromasters in Marketing Analytics from UC Berkeley and statistical analysis in Life Sciences specialization from Harvard. I am chapter lead/Co-Organizer of Women in Machine Learning and Data Science Bengaluru Chapter and Core organizing team member at WIDS Bengaluru .I have around 6 years of technical experience working in various companies like Infosys, Temenos, NeoEYED and Mysuru Consulting Group. I am part of dedicated group of experts and enthusiasts who explore Coursera courses before they open to the public, an ambassador at AIMed (an initiative which brings together physicians and AI experts), part time Data science instructor, mentor at GLAD (gladmentorship.com), mentor at JobsForHer and volunteer at Statistics without Borders. I developed the course curriculum for Probabilistic Graphical Models @ Upgrad which is taught by Professor Srinivasa Raghavan from IIIT Bangalore.