Theme and format
The Fifth Elephant 2017 is a four-track conference on:
- Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
- Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
- Hands-on tutorials on data mining tools, and ML platforms and techniques.
- Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.
About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
- Full-length, 40-minute talks.
- Crisp, 15-minute talks.
- Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
- Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
- Off-the-record (OTR) sessions of 60-90 minutes duration.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or 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.
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for 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), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
- Deadline for submitting proposals: June 10
- First draft of the coference schedule: June 20
- Tutorial and workshop announcements: June 20
- Final conference schedule: July 5
- Conference dates: 27-28 July
For more information about speaking proposals, tickets and sponsorships, contact email@example.com or call +91-7676332020.
Modeling intent of the user using Probabilistic Machine Learning
Understanding the user’s intent can help the product team dramatically improve the user’s experience. Be it adding the right products to a shopping cart, stocks to the portfolio or packages to a software stack, the user’s intent drives the choices and products added. When designing recommendation systems, modelling intent is non-trivial. The intent behind the action is hidden. This talk is about how the speaker used probabilistic machine learning to model intent.
** Mad Hatter: Can you find a needle in the haystack?
Alice: Yup - if I know that it’s an iron needle. Give me the magnet! **
Consider the case of a new developer navigating the technology landscape to pick the libraries required to build her software application, or a new bride-to-be planning her bridal outfit on an e-commerce website.
What’s common? The dilemma - what’s the right set of choices that will click; the choices that will help them succeed in their intent.
In the world of Machine Learning, Recommendation Systems are widely used to solve the above problem. But the platform hosts a long tail to choose from. How could I make the recommendation system work?
By modelling intent first.
A two-stage model was built.
1. At the first stage, based on the user’s metadata, unsupervised clustering algorithm was employed to segment the users. This will help answer who the user is
2. For each user type, probabilistic machine learning models were used.
The talk discusses:
- The common problem when modelling these kinds of problems from start
- How to handle cold-start scenario? (no data)
- How to scale the models when data scales - both in volume and velocity ?
- How to automate intent identification?
- Why Bayesian models?
A real-time demo of the application that’s hosted on OpenShift will be showcased.
** Key Takeaways:**
- Acknowledge the fuzziness in determining the domain
- Learn how this information can be used to improve the user experience.
- See an example of ML in enhancing the product experience.
- A Machine Learning pipeline for solving a problem that’s stated only implicitly.
Sarah is an engineer at Red Hat where she works on developer-oriented analytic projects. Her bachelor’s thesis on Topics Modeling was presented at Ninth International Conference on Contemporary Computing. She is currently a mentor with the Next Scholars Program and the Global Give Back Circle. With her mentorship work, she hopes to increase the participation of women in tech. She also volunteers her time with Women Who Code, Lean In India, and Systers. She is ever enthusiastic about Data Science, Women in STEM, and Open Source.