Schedules

Event Schedule

We are in the process of finalizing the sessions. Expect more than 40 talks at the summit. Please check back this page again.

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  • Day 1

    January 30, 2019

  • Feature selection in large diimensional spaces is a staple of machine learning. While these can be done in several ways (using covariance matrices, mutual inforrmation, SVD, auto-encoders, MCMCs to name a few methods), we propose using genetic algorithms with a multi-dimensional covariance map to solve the feature selection problem. Because we actively trace and reward benefecial parametric co-occurrences using a Bayesian type conditional cross-over and mutation, we are able to significantly reduce the number of generations required to convergence. I will demonstrate the use of this algorithm in conjunction with SVMs to solve two problems in astronomy.
    Tech Talks

  • Lots of good engineering practices (automation, obsessive focus on productivity, etc) are a vital part of building a functional data science org.
    Keynotes

  • With the increase of computing power and availability of data - supervised algorithms are solving efficiently most of classification problems in text. However problems like entity recognition still remains to be an area of craft and domain knowledge. For instance in a chatbot - the 'what' part is easily recognized with the right ML model and right amount of data. The 'Who' of it is still a challenge. In the talk - we will take a deeper look at named entity recognition, the stack of techniques, their applications, strategies and real-life challenges while applying those.
    Tech Talks

  • Day 2

    January 31, 2019

  • In the last few years, interest in AIML has exploded and spilled over to almost all industries and domains. There is hardly any industry which is deemed safe from disruption by AI. Recent advances in AI/ ML/ DL have pushed the boundaries on what was considered possible and newer product and services which were unthinkable few years ago. This has created huge demand for so called AI/ ML/ DL product and services. But is AI for everyone? Can it be made available to everyone? In our master class, we will take a look at what ingredients are needed to put the power of AI in hands of everyone. In particular, we will look at the open source tools and techniques which can be accessed by anyone aspiring to become data scientist/ AIML professional. In the session we will discuss the open source alternatives across the entire project life cycle. We will also discuss how the techniques have evolved over time and use cases for key techniques. Lastly, we will also look at the road blocks which may prevent successful democratization of AI.
    Masterclasses

  • Deep Learning (DL) researchers use GPUs and CPUs to training their models. The target deployment environment introduces various challenges that are typically not present in the training environment. How to increase the performance of trained DL model during inference phase? Dr. Sunil shall present various options for improving performance at Inference stage. He describes Intel’s inference engine as one option to run DL models in Caffe/Tensorflow/Keras using CPUs. He discusses implementation for two deep learning based healthcare models and share results on the performance benchmarks. He highlights how the CPUs/USB stick can be used to its maximum potential and still not compromising on the quality of output at exceptional speeds.
    Tech Talks

  • The expected growth in AI business is simply not happening at the anticipated rate. Consequently, if someone is an AI professional, there’re not many big projects that are coming their way. I’d like to talk about why this is the case, and if you’re an AI professional, how to prepare.” The first challenge is lack of skillset in the IT employee base. Are employers upskilling their employees? Are they investing enough? AI requires specialized knowledge: statistics, algorithms and computer science, not civil or mechanical engineering. So, well-intentioned employees starting on the AI journey get stuck. Additionally, there is only a small talent pool. 500,000 jobs are available in AI, but only 50,000 computer science grads per year, out of which only 20-30% are of some quality. Second, companies need to pick processes carefully that they want to incorporate AI into. If not, maybe RPA or analytics is a better fit. For example, for routine invoice processing, payroll processing, etc., RPA can fit the bill nicely. Third, the lack of infrastructure sets us back as a country in terms of AI adoption. Basic telephone access and WiFi are missing; how can we talk about drones and video processing in AI? If you’re an AI professional, the key is to learn the specific skills, and encourage your company to select incoming employees from the computer science, applied mathematics or statistics background. Second, help your business leaders select the correct business processes for AI enablement. Finally, learn to work with the reality of low-quality infrastructure, and pick use cases that have a high chance of success even with the inferior infrastructure.
    Keynotes

  • As part of this topic will cover the following details Planning a scalable lifecycle for analytical models (POC to Pilot to Production) Planning for scalable software layer (R vs Python vs Scala; MapReduce) Planning for scalable hardware layer (achieving the cost vs performance trade-offs) Planning for scalable Devops (kubernetes based deployment path) Checklist for success in deploying Analytics models at scale (dos and donts)
    Tech Talks

  • In the current competitive market scenario, using only linear models to solve predictive modeling problems will leave us behind. The trend is moving towards using the machine learning and deep learning algorithms in analytics industry to solve predictive modeling problems because of their low bias, higher predictive power and dependencies on more features. The real challenge is to explain these black box algorithms. In this talk, I will discuss different ways of explaining machine learning models.
    Keynotes

Extraordinary Speakers

Meet the best Machine Learning Practitioners & Researchers from the country.

  • Early Bird Pass (Expired)

    Available from 16th Nov to 14th Dec 2018
  • Access 1-3 Stages
  • Access 50+ Speakers
  • Access to Exhibition Area
  • Access to food & beverage
  • All access, 2-day pass
  • Group discount available
  • 6,500 + taxes
  • Late Pass

    Available from 18th Jan Onwards
  • Access 1-3 Stages
  • Access 50+ Speakers
  • Access to Exhibition Area
  • Access to food & beverage
  • All access, 2-day pass
  • 10,000 + Taxes