The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.
Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.
We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.
We are looking for application level and tool-centric talks and tutorials on the following topics:
- Deep Learning
- Text Mining
- Computer Vision
- Social Network Analysis
- Large-scale Machine Learning (ML)
- Internet of Things (IoT)
- Computational Biology
- ML in healthcare
- ML in education
- ML in energy and ecology
- ML in agriculrure
- Analytics for emerging markets
- ML in e-governance
- ML in smart cities
- ML in defense
The deadline for submitting proposals is 30th April 2016
This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:
- Full-length 40 minute talks.
- Crisp 15-minute talks.
- Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on Workshop sessions, 3 and 6 hour duration.
Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.
We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.
We will notify you about the status of your proposal within three weeks of submission.
Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).
Commitment to open source
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
Key dates and deadlines
- Revised paper submission deadline: 17 June 2016
- Confirmed talks announcement (in batches): 13 June 2016
- Schedule announcement: 30 June 2016
- Conference dates: 28-29 July 2016
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.
For more information about speaking proposals, tickets and sponsorships, contact firstname.lastname@example.org or call +91-7676332020.
Though visualisation is used in data science to understand the shape of the data (data-vis), it is not widely used for the models developed; which are largely evaluated based on numerical summaries. Model visualisation (model-vis) can help understand: the shape of the model, the impact of parameters & different input data on the model, the fit of the model & where it can be improved.
Data science is a process of abstraction. In order to explain or to predict a real phenomena, the process start with framing the problem, acquiring & refining the data and then moves between the three layers of abstraction - transformations (data abstraction), visualisations (visual abstraction) and modelling (symbolic abstraction). All these three layers of abstraction work together to try and build a truer (or more closer) representation of the real phenomena.
Data visualisation (data-vis) helps us to understand the portrait and the shape of the data. The science of data-vis for exploratory data analysis is well developed, for both static graphics (scatter plot matrices, glyph based approaches, geometric transforms like parallel coordinates) and interactive graphics (layering, brushing and linking, projections and tours). See my talk at Fifth Elephant 2015 on Visualising Multi Dimensional Data - https://www.youtube.com/watch?v=X8rNDvPNg30. However, the power of visualisation is rarely leveraged for understanding the models developed better. Model evaluation is still largely restricted through numerical summaries. Extending visualisation to model building can be a powerful way to improve our understanding of the model.
Model visualisation (model-vis) can help us to understand the shape of the model and compare it to the shape of the data. It allows to see the fit of the model and understand where the fit can be improved. It also allows us to better understand the parameters in the model and how the model changes when the parameters change as well as how the parameters changes when the input data changes.
The science and tools for model-vis are still very under-developed. This talks looks at practical examples of doing model-vis in regression (linear, lasso), classification (logistic, trees, LDA) and clustering (hierarchical) problems that can help us better understand the model. This includes exploring model-vis approaches that:
- Visualise the model in data space as opposed to data in model space
- Visualise the entire space of models
- Visualise the same model with varying tuning parameters
- Visualise the same model with different input datasets
- Visualise the process of model fitting as opposed to final result
Integrating these approaches for model-vis as a part of model evaluation will strengthen the understanding of the model and lead to better model building for a data scientist. Model-vis can then complement data-vis for fitting better models as well as for communicating the insight from the data science process.
Post this talk, the audience will have a better understanding of the power of visualisation beyond data-vis to model-vis and use it to build better models as a data scientist.
A basic understanding of the data science process - Frame the problem, Acquire the data, Refine the data, Explore the data, Model the solution and Communicate the Insight.
Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. He uses storytelling and data visualization as tools for improving communication, persuasion and leadership. He conducts workshops and trainings on data visualisation and data science for corporates, non-profits, colleges, and individuals at narrativeVIZ Consulting. He also teaches storytelling with data as invited faculty in management schools e.g. IIM Bangalore, IIM Ahmedabad and design schools e.g. NID Bangalore.
His background is in strategy consulting in using data-driven stories to drive change across organizations and businesses. He has more than 14 years of management consulting experience, first with AT Kearney in India, then with Booz & Company in Europe and more recently for startups in Bangalore. He did his B.Tech in Mechanical Engineering from IIT, Delhi and PGDM (MBA) from IIM, Ahmedabad. You can find more about him at amitkaps.com and tweet him at @amitkaps.
- Visualising Multi Dimensional Data - https://www.youtube.com/watch?v=X8rNDvPNg30
- Learning Djembe Visually - https://www.youtube.com/watch?v=hA4sF02Ib0Q