The Fifth Elephant 2015
A conference on data, machine learning, and distributed and parallel computing
Jul 2015
13 Mon
14 Tue
15 Wed
16 Thu 08:30 AM – 06:35 PM IST
17 Fri 08:30 AM – 06:30 PM IST
18 Sat 09:00 AM – 06:30 PM IST
19 Sun
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:
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:
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.
Hosted by
Harshad Saykhedkar
@harshss
Submitted May 25, 2015
If you have ever been in a “black box” operating mode where you are throwing more data/complex models at a machine learning problem without a clue about why it is working or not working, this workshop is for you! The workshop will primarily focus on understanding supervised machine learning.
Here’s a mind map showing the overall picture of what will be covered in the workshop.
This will be a 4 hour workshop with a short break in the middle. The broad outline is as follows,
How much machine learning should I know already?
We expect you to know bare minimum basics like supervised Vs. unsupervised machine learning model. If you know what is a linear regression, it should be good enough.
I don’t know Python. Is this workshop for me?
Yes. As long as you know basics of programming and have written atleast some code in any language.
How much programming should I know to attend?
You should know basic programming (loops, conditional expressions, variable assignments, reading files, performing some data manipuation on them).
Why not cover unsupervised learning/semi-supervised learning/some other fancy model X?
We will focus on depth and try to cover few topics well.
Will the workshop cover Apache Spark/Hadoop/Mahout or X library/ecosystem?
No. This is an ideas/algorithms talk and libraries will just serve as means for understanding. Different libraries/ecosystems are likely to be covered in depth by other speakers.
What about the data and the code to be used at the time of workshop?
We will using this github repository to share code and data. Please make sure that you clone this repository or download the data folder beforehand. You can also download the data from UCI ML repository page here.
What will be the choice of libraries and language?
Can I install some of the dependencies at the time of workshop?
Big No. Internet support might be shacky. Also, these libraries are pretty heavy. It will not be possible to download and install them at the time of workshop. So make sure that all dependencies are installed before hand.
Can I use a Windows based machine?
Sure, as long as you get all the dependencies installed before the workshop. Given time limitations, we won’t have any installation support at the time of workshop.
Build dependencies for scikit-learn
Scikit-learn depends on some C libraries. The installation instructions given on the page listed above covers installation of dependencies very well. Please refer to those.
Harshad leads the machine learning and data team @ Sokrati, an advertising technology and analytics company based out of Pune. He has spent 6 years in applying statistical models in variety of domains like insurance, banking, telecom and advertising. He has experience with many tools in the data ecosystem like Python, R, Clojure, Hadoop, Spark etc. He spends time learning theory and applications of machine learning models from simple regression to deep learning. Harshad holds a master’s degree from Indian Institute of Techonology, Mumbai.
http://www.slideshare.net/HarshadSaykhedkar/ml-workshop-jul2015mm
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