The Fifth Elephant 2015

A conference on data, machine learning, and distributed and parallel computing

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.

Track 1: Discovering Insights and Driving Decisions

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.

Track 2: Speed at Scale

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

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 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.

Workshops

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.

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Swaroop Krothapalli

@swaroop

Ensemble Learning

Submitted Jun 14, 2015

To understand most basic and convenient approaches of ensembling

Outline

Ensemble learning is all about combining predictions from different machine learning techniques in order to create a stronger overall prediction. For example, the predictions of a random forest, a support vector machine, and a simple linear model may be combined to create a stronger final prediction set. The key to creating a powerful ensemble is model diversity. An ensemble with models that are very similar in nature will perform lower than a more diverse model set.

Ensemble learning can be broken down into two tasks: developing a population of base learners from the training data, and then combining them to form the composite predictor.In this talk I would like to give a basic overview of ensemble learning. And discuss about building an ensemble model by conducting a regularized and supervised search in a high-dimensional space of weak learners.

No wonder most of the winning submissions in Kaggle are ensemble models.

Speaker bio

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.

https://in.linkedin.com/in/kswaroop

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Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more