Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

Propose a session

Introduction to Bayesian Networks(3 hour workshop)

Submitted by usha rengaraju (@usharengaraju) on Sunday, 28 April 2019


Preview video

Section: Workshops Technical level: Beginner Session type: Workshop

Abstract

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.

Outline

Detailed Breakdown of the workshop
1.Probability Primer
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)

Requirements

Laptop

Speaker bio

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

https://www.amazon.com/Hands-Markov-Models-Python-probabilistic/dp/1788625447

https://www.amazon.com/Mastering-Probabilistic-Graphical-Models-Python/dp/1784394688

He is the founding member and significantly contributed to pgmpy package.

Usha Rengaraju
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.

Links

Slides

https://drive.google.com/file/d/1eAged2YmqQ3c120NtMV1L3G_hs3GbYa1/view?usp=sharing

Preview video

https://www.youtube.com/watch?v=Vcmjqx7lht0&t=3s

Comments

  • Abhishek Balaji (@booleanbalaji) Reviewer 2 months ago

    Thanks for submitting a proposal. Someone from our team will be in touch with you to understand more about the proposal.

    • usha rengaraju (@usharengaraju) Proposer 2 months ago

      Sure Abhishek . Kindly let us if you need additional details on the proposal. I have uploaded a preview video of Abinash presenting in Scipy conference on pgmpy package.

  • Zainab Bawa (@zainabbawa) Reviewer 2 months ago

    Usha, edit this proposal and resubmit with the following details:

    1. Background knowledge required to participate in the workshop. What concepts/technologies should participants be familiar with in order to attend the workshop.
    2. Target audience: who should attend the workshop? Specify personas rather than mentioning beginner or advanced level audiences. Ensure that the workshop caters to one specific audience segment only.
    3. Who should NOT attend this workshop.
    4. Why attend this workshop? What will participants learn from attending this workshop? How will they benefit?
    5. Detailed workshop plan. Give us a break-up of the different sections of the workshop and what content will be covered in each section.
    6. Requirements. What software and other tech should participants install on their laptops before coming to this workshop? Should participants carry laptops with specific configurations on their machines?

    The above details should be added to this proposal by or before 26 May so that we can review the details and close on the decision.

    • usha rengaraju (@usharengaraju) Proposer a month ago

      Updated

  • Zainab Bawa (@zainabbawa) Reviewer 2 months ago

    Besides the above, please respond to the following comments:

    1. Have you conducted this workshop before? If yes, share details.
    2. The preview video has only one instructor whereas two instructors are mentioned here. We need to see a preview video with both instructors.

Login with Twitter or Google to leave a comment