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.
Anatomy of Decision Trees using an example from Kaggle
Decision trees are amongst the most popular predictive modelling techniques in the analytics industry. Attendees will learn how to effectively apply decision trees to predict survival on the Titanic: Machine Learning from Disaster problem in Kaggle.
Attendees will learn about practical considerations for predictive modelling using Decision Trees, Bagging, Boosting, Random Forest using a simple example from Kaggle. The session will include slide presentation accompanied with demonstration using R and Rapidminer. Participants should be familiar with basics of predictive analytics including classification.
Attendees would not need to bring anything.
Saurabh is a Senior Specialist at Sapient Global Markets. He has 20 years of industry experience in technology consulting and product development in USA and India. Saurabh’s domain experience includes Financial Services, Healthcare and Telecom. In his current role, he is responsible for driving sales, delivery and innovation efforts in Data Analytics space at Sapient Global Markets.
Prior to Sapient, Saurabh worked with PwC as Associate Director, Strategy & Architecture practice. He is passionate about machine learning and understanding its impact on businesses, world economy and future generations.