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.
Tackling ML's black boxes with probabilistic programming
While machine learning has become a wildly popular solution for analyzing a lot of problems, it’s also ended up becoming a major black box. The objective of this talk is to showcase probabilistic programming as a feasible alternative in such scenarios.
As mentioned above, machine learning has now become a platform where data is passed through an algorithm, which is essentially a black box - out pop predictions, but nobody’s got any idea what exactly happened, during the analysis phase. However, probabilistic programming tries to solve this problem by introducing a smaller Bayesian inference engine, with open models on top of it.
The talk shall encompass a brief description of the history behind Bayesian analysis and its stance vis-a-vis traditional machine learning, as well as two sample scenarios - one involves running an A/B test on a web app and running probabilistic models on the data collected during the test. The second scenario is about two market trading strategies, and using Bayesian statistics and Monte Carlo Markov chains to estimate the chances of each strategy beating the market. The entire emphasis here is on using probabilistic programming to tell a generative story with data.
All the code here will be in Python, with heavy usage of PyMC3. If possible, I would also love to throw in some Bayesian models written in Julia.
Some familiarity with Python, and probability theory.
I’m Rudraksh, and I specialize in computational math. I’ve got varied experience in using math and data science for journalism, events management as well as ed-tech and social media startups. I’ve also co-founded a startup called MathHarbor, where we’re building a cloud platform and hub for computational math and stats using open-source languages and toolsets, as well as consulting for the Indian Army on combat wargaming.