Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
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 license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize 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.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Building Recommender system
Will talk about classical and state-of-the-art recommender systems. The audience will also get a flavour of the mathematical computations that go into recommender systems.
Recommender Systems solves matrix sparsity problem. And this idea of predicting sparse values can be applied for various problems across domains. I have used recommender systems to identify audience clusters for a conference, recommending new jokes to users based on the past jokes they liked, and few kaggle problems.
One of the key events that energized research in recommender systems was the Netflix prize. Netflix sponsored a competition, that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company’s existing recommender system.
Recommender systems typically produce a list of recommendations in one of the two ways - through collaborative or content-based filtering. Would like to cover both of them with the implementation and mathematics involved.
Familiarity with Matrices and basic algebra
Currently part of data science team at Fidelity Investments, Business Analytics and Research.
Master’s in Mathematics from BITS, Pilani.
Am an open-source enthusiast and Kaggler.