{ 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.


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 1 year ago

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    • 45 Mins

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      This talk will demonstrate how Julia is increasingly becoming a natural language for machine learning, the kind of libraries and applications the Julia community is building, the contributions from India (there are many!), and our plans going forward.

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      45 Mins

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      To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

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      45 Mins

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      The aim of this talk, will be to offer a practical overview of the above aspects of causal inference -which in turn as a discipline lies at the fascinating confluence of statistics, philosophy, computer science, psychology, economics, and medicine, among others. Topics include:

      • The fundamental tenets of causality and measuring causal effects.
      • Challenges involved in measuring causal effects in real world situations.
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      • Provide an introduction to measuring causal effects using observational data using matching and its extension of propensity score based matching with a focus on the a) the intuition and statistics behind it b) Tips from the trenches, basis the speakers experience in these techniques and c) Practical limitations of such approaches
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      • Finally conclude with why understanding having a nuanced understanding of causality is all the more important in the big data era we are into.
    • Dr. C.S.Jyothirmayee

      Dr. C.S.Jyothirmayee / Usha Rengaraju / Vijayalakshmi Mahadevan - Deep learning powered Genomic Research

      90 Mins

      The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

      With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

      Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

      Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

      There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

      This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

    • Badri Narayanan Gopalakrishnan

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      45 Mins
      Case Study

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    • Akshay Bahadur

      Akshay Bahadur - Minimizing CPU utilization for deep networks

      Akshay Bahadur
      Akshay Bahadur
      Symantec Softwares
      schedule 1 year ago
      Sold Out!
      45 Mins

      The advent of machine learning along with its integration with computer vision has enabled users to efficiently to develop image-based solutions for innumerable use cases. A machine learning model consists of an algorithm which draws some meaningful correlation between the data without being tightly coupled to a specific set of rules. It's crucial to explain the subtle nuances of the network along with the use-case we are trying to solve. With the advent of technology, the quality of the images has increased which in turn has increased the need for resources to process the images for building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact.
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    • Favio Vázquez

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      90 Mins

      Cleaning, preparing , transforming, exploring data and modeling it's what we hear all the time about data science, and these steps maybe the most important ones. But that's not the only thing about data science, in this talk you will learn how the combination of Apache Spark, Optimus, the Python ecosystem and Data Operations can form a whole framework for data science that will allow you and your company to go further, and beyond common sense and intuition to solve complex business problems.

    • Pankaj Kumar

      Pankaj Kumar / Abinash Panda / Usha Rengaraju - Quantitative Finance :Global macro trading strategy using Probabilistic Graphical Models

      90 Mins

      { This is a handson workshop in pgmpy package. The creator of pgmpy package Abinash Panda will do the code demonstration }

      Crude oil plays an important role in the macroeconomic stability and it heavily influences the performance of the global financial markets. Unexpected fluctuations in the real price of crude oil are detrimental to the welfare of both oil-importing and oil-exporting economies.Global macro hedge-funds view forecast the price of oil as one of the key variables in generating macroeconomic projections and it also plays an important role for policy makers in predicting recessions.

      Probabilistic Graphical Models can help in improving the accuracy of existing quantitative models for crude oil price prediction as it takes in to account many different macroeconomic and geopolitical variables .

      Hidden Markov Models are used to detect underlying regimes of the time-series data by discretising the continuous time-series data. In this workshop we use Baum-Welch algorithm for learning the HMMs, and Viterbi Algorithm to find the sequence of hidden states (i.e. the regimes) given the observed states (i.e. monthly differences) of the time-series.

      Belief Networks are used to analyse the probability of a regime in the Crude Oil given the evidence as a set of different regimes in the macroeconomic factors . Greedy Hill Climbing algorithm is used to learn the Belief Network, and the parameters are then learned using Bayesian Estimation using a K2 prior. Inference is then performed on the Belief Networks to obtain a forecast of the crude oil markets, and the forecast is tested on real data.

    • Akash Tandon

      Akash Tandon - Traversing the graph computing and database ecosystem

      Akash Tandon
      Akash Tandon
      Data Engineer
      schedule 1 year ago
      Sold Out!
      45 Mins

      Graphs have long held a special place in computer science’s history (and codebases). We're seeing the advent of a new wave of the information age; an age that is characterized by great emphasis on linked data. Hence, graph computing and databases have risen to prominence rapidly over the last few years. Be it enterprise knowledge graphs, fraud detection or graph-based social media analytics, there are a great number of potential applications.

      To reap the benefits of graph databases and computing, one needs to understand the basics as well as current technical landscape and offerings. Equally important is to understand if a graph-based approach suits your problem.
      These realizations are a result of my involvement in an effort to build an enterprise knowledge graph platform. I also believe that graph computing is more than a niche technology and has potential for organizations of varying scale.
      Now, I want to share my learning with you.

      This talk will touch upon the above points with the general premise being that data structured as graph(s) can lead to improved data workflows.
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    • Shalini Sinha

      Shalini Sinha / Ashok J / Yogesh Padmanaban - Hybrid Classification Model with Topic Modelling and LSTM Text Classifier to identify key drivers behind Incident Volume

      45 Mins
      Case Study

      Incident volume reduction is one of the top priorities for any large-scale service organization along with timely resolution of incidents within the specified SLA parameters. AI and Machine learning solutions can help IT service desk manage the Incident influx as well as resolution cost by

      • Identifying major topics from incident description and planning resource allocation and skill-sets accordingly
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      • Analyzing Root Causes of incidents and introducing processes and automation framework to predict and resolve them proactively

      We will look at different approaches to combine standard document clustering algorithms such as Latent Dirichlet Allocation (LDA) and K-mean clustering on doc2vec along-with Text classification to produce easily interpret-able document clusters with semantically coherent/ text representation that helped IT operations of a large FMCG client identify key drivers/topics contributing towards incident volume and take necessary action on it.

    • Saikat Sarkar

      Saikat Sarkar / Dhanya Parameshwaran / Dr Sweta Choudhary / Srikanth Ramaswamy / Usha Rengaraju - AI meets Neuroscience

      480 Mins

      This is a mixer workshop with lot of clinicians , medical experts , Neuroimaging experts ,Neuroscientists, data scientists and statisticians will come under one roof to bring together this revolutionary workshop.

      The theme will be updated soon .

      Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.


      { This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

      { We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

      Abstract for the Neuroimaging Data Science Part of the workshop:

      The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

      The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

    • Chaitanya Krishna Thanneeru

      Chaitanya Krishna Thanneeru - Taxonomy Building using ML

      45 Mins
      Case Study

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      Latent semantic analysis has been shown to be ideal for quickly clustering the document space. Applied in a hierarchical manner on top-level clusters to derive child clusters and informed with inputs from the subject matter experts and taxonomists, namely taxonomy terms and synonyms, makes it possible to get a sense of the coverage in the content space against the enterprise taxonomy model.
      Where there are shortcomings, additional training data needs to be obtained in order to effectively build auto-tagging solutions. One technique for data augmentation is query formulation, again utilizing entity extraction from owned content along with the taxonomy categories and synonyms, to construct social listening streams to surface new off-property content to become part of the training corpus.

    • Kshitij Srivastava

      Kshitij Srivastava / Manikant Prasad - Data Science in Containers

      45 Mins
      Case Study

      Containers are all the rage in the DevOps arena.

      This session is a live demonstration of how the data team at Milliman uses containers at each step in their data science workflow -

      1) How do containerized environments speed up data scientists at the data exploration stage

      2) How do containers enable rapid prototyping and validation at the modeling stage

      3) How do we put containerized models on production

      4) How do containers make it easy for data scientists to do DevOps

      5) How do containers make it easy for data scientists to host a data science dashboard with continuous integration and continuous delivery

    • Dr. Neha Sehgal

      Dr. Neha Sehgal - Open Data Science for Smart Manufacturing

      45 Mins

      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.

    • Saurabh Jha

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

      480 Mins

      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
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      tricks to train a deep neural network models. It will be hands on session where
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      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
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      will try and answer some of the hard questions like how many layers must be
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      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

    • Gopinath Ramakrishnan

      Gopinath Ramakrishnan - Five Key Pitfalls in Data Analysis

      45 Mins

      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.

    • Vidhya Veeraraghavan

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

      45 Mins
      Case Study

      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.

    • Shankar Somayajula

      Shankar Somayajula - Revisiting Market Basket Analysis (MBA) with the help of SQL Pattern Matching