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 firstname.lastname@example.org or call +91-7676332020.
A Recommender for Match-making: Item-based CF, PageRank, Evaluation techniques & Deep-Learning
Online match-making has a lot of challenges where Machine-Learning can help. When we look at a profile what is it that makes us swipe right or left? Is there something about a profile that attracts us and if so what can a person’s historical interactions say about their preferences.
I believe the contents would resonate with the audience quite well and help them appreciate the challenges of doing machine-learning to help in finding love in the digital era.
I worked at Coffeemeetsbagel.com, when I had a chance to build a recommendation engine which had all the interesting ingredients: Item-based collaborative filtering, PageRank and also Deep-Learning. Building it was a very fun and exciting activity since it seemed like unraveling the laws of attraction between people. This variant has the added complexity that the recommendation needs to work both ways. Choosing the right input data was key. Evaluating the recommender using the usual MAE was not sufficient. I needed to also consider Precision@N and a few other metrics. PageRank certainly helped in this regard. The Cold-start problem still persisted and Deep-Learning came to the rescue. Finally scalability was a real concern. Fortunately we had chosen the right tools.
Did we make an impact? We will know in a few months. The Recommender is on A/B test in a city near you.
My name is Prabhakar Srinivasan. I am currently working for Apple as a Data Scientist. I would like to submit this topic to FifthElephant on Recommendation Engines. I worked on this topic when I was employed at Coffeemeetsbagel.
Over the last decade, I gained experience working on Recommendation Engines,
and Deep-Learning and Supervised and Unsupervied Machine-learning techniques. I was able to successfully develop ML products for companies like Cisco, Coffeemeetsbagel and Apple.
I would like to share my experiences and knowledge in this space with the audience.