{ This is a handson workshop . The use case is Traffic analysis . }

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, biomonitoring, 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 , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

 
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Outline/Structure of the Workshop

1. Probability Primer

2. Bayesian Networks

3. Independence in Bayesian Networks (covers d seperation, hammersley clifford)

4. Inference (covers Variable Elimination)

5. Missing data (Expectation Maximization)

6. Case Study using Bayesian networks

Learning Outcome

At the end of the Bayesian Network workshop, one would be able to

  • understand the probabilistic principles of reasoning under uncertainty
  • have insight into algorithms for probabilistic reasoning in Bayesian networks

Target Audience

Data Scientists, Data Analysts, Deep Learning Engineers, Statisticians, Health-science professionals,Machine Learning Engineers,

Prerequisites for Attendees

Basics of probability.

schedule Submitted 3 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  2 months ago
    reply Reply

    Dear Usha: What kind of fault detection in semiconductor manufacturing are we discussing in the talk? Is it etching defects, doping errors, errors at electrical test, shifts in process, variation across the wafer/lot?

    Were Bayesian methods applied at a particular company or research institution successfully for this use case by the authors?

    Which of the three authors will be the primary presenter and will be presenting at the conference? If all will be present, that is great - please add which parts of the topics they will cover and for how long.

    Warm Regards,

    Vikas


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    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

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    Intermediate

    Open Data offers a tremendous opportunity in transformation of today’s manufacturing sector to smarter manufacturing. Smart Manufacturing initiatives include digitalising production processes and integrating IoT technologies for connecting machines to collect data for analysis and visualisation.

    In this talk, an understanding of linkage between various industries within manufacturing sector through lens of Open Data Science will be illustrated. The data on manufacturing sector companies, company profiles, officers and financials will be scraped from UK Open Data API’s. The work I plan to showcase in ODSC is part of UK Made Smarter Project, where the work has been useful for major aerospace alliances to find out the champions and strugglers (SMEs) within manufacturing sector based on the open data gathered from multiple sources. The talk includes discussion on data extraction, data cleaning, data transformation - transforming raw financial information about companies to key metrics of interest - and further data analytics to create clusters of manufacturing companies into "Champions" and "Strugglers". The talk showcased examples of powerful R Shiny based dashboards of interest for suppliers, manufacturer and other key stakeholders in supply chain network.

    Further analysis includes network analysis for industries, clustering and deploying the model as an API using Google Cloud Platform. The presenter will discuss about the necessity of 'Analytical Thinking' approach as an aid to handle complex big data projects and how to overcome challenges while working with real-life data science projects.

  • Liked Saurabh Jha
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    Saurabh Jha / Rohan Shravan / Usha Rengaraju - Hands on Deep Learning for Computer Vision

    480 Mins
    Workshop
    Intermediate

    Computer Vision has lots of applications including medical imaging, autonomous
    vehicles, industrial inspection and augmented reality. Use of Deep Learning for
    computer Vision can be categorized into multiple categories for both images and
    videos – Classification, detection, segmentation & generation.
    Having worked in Deep Learning with a focus on Computer Vision have come
    across various challenges and learned best practices over a period
    experimenting with cutting edge ideas. This workshop is for Data Scientists &
    Computer Vision Engineers whose focus is deep learning. We will cover state of
    the art architectures for Image Classification, Segmentation and practical tips &
    tricks to train a deep neural network models. It will be hands on session where
    every concepts will be introduced through python code and our choice of deep
    learning framework will be PyTorch v1.0 and Keras.

    Given we have only 8 hours, we will cover the most important fundamentals,
    current techniques and avoid anything which is obsolete or not being used by
    state-of-art algorithms. We will directly start with building the intuition for
    Convolutional Neural Networks, and focus on core architectural problems. We
    will try and answer some of the hard questions like how many layers must be
    there in a network, how many kernels should we add. We will look at the
    architectural journey of some of the best papers and discover what each brought
    into the field of Vision AI, making today’s best networks possible. We will cover 9
    different kinds of Convolutions which will cover a spectrum of problems like
    running DNNs on constrained hardware, super-resolution, image segmentation,
    etc. The concepts would be good enough for all of us to move to harder problems
    like segmentation or super-resolution later, but we will focus on object
    recognition, followed by object detections. We will build our networks step by
    step, learning how optimizations techniques actually improve our networks and
    exactly when should we introduce them. We hope the leave you in confidence
    which will help you read research papers like your second nature. Given we have
    8 hours, and we want the sessions to be productive, we will instead of introducing

    all the problems and solutions, focus on the fundamentals of modern deep neural
    networks.

  • Liked Gopinath Ramakrishnan
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    Gopinath Ramakrishnan - Five Key Pitfalls in Data Analysis

    45 Mins
    Talk
    Beginner

    Data Science is all about deriving actionable insights through data analysis.
    There is no denying the fact that such insights have a tremendous business value.
    But what if -
    Some crucial data has been left out of consideration ?
    Wrong inferences have been drawn during analysis ?
    Results have been graphically misrepresented?
    Imagine the adverse impact on your business if you take wrong decisions based on such cases.

    In this talk we will discuss the following 5 key pitfalls to lookout for in the data analysis results before you take any decisions based on them
    1. Selection Bias
    2. Survivor Bias
    3. Confounding Effects
    4. Spurious Correlations
    5. Misleading Visualizations

    These are some of the most common points that are overlooked by the beginners in Data Science.

    The talk will draw upon many examples from real life situations to illustrate these points.

  • Liked Vidhya Veeraraghavan
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    Vidhya Veeraraghavan - Story Teller - Analytics in Banking & Financial Sector

    45 Mins
    Case Study
    Beginner

    As kids, we always enjoyed stories. Some scary, some holy, some imbibing moral values & some just for fun.

    Analytics is fun when you approach it with passion and curiosity. I know this because I have done this. With few case studies, I wish to illuminate your wits about Analytics and how it is being actively used in Banking and Financial Sector.

    Come join me for a fun ride.