Deconstructing Linear Regression

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

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NIMHANS Convention center

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.

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The Fifth Elephant

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

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Submitted Jun 14, 2015

Section: Crisp Talk Technical level: Beginner

This short talk aims to “deconstruct” Linear Regression and explain the steps done by the library functions before throwing out the intercept and slope.

Outline #

We usually use linear regression when we know that our dependent variable has a linear relationship with the independent variable. We use the library functions to identify the parameters and move on.

But how does the library function choose a single line, out of the infinite possibilities?
How does it know that the line it chooses is the one that fits the data best?
Or rather, what is a best fit, in the first place?
What if the technique it uses has some inherent flaws... Can that guide me to make a smarter choice of model instead?

Have these questions come to your mind? Are you still in search of the answers? If yes, this talk is for you.
We won’t go into the depths of all the techniques and everything under the sky about Linear Regression.
We’ll just the scratch the surface and digest one small chunk of Simple Linear Regression.
Enough though, to get the curious minds wiggling !!!

Requirements #

Particpants: Familiarity with fundamental calculus.
Infra: White-board, duster and markers.

Speaker bio #

I am a java programmer and a stats/ math enthusiast with 10+ years of coding experience. With nearly 5 years of working with Data Scientists and Wildlife biologists.
As much as I love learning techniques (like Linear-Regression! for instance), I also love to learn about derivations and philosophy involved. And just in case it is not evident, I love to talk about that too.