Anthill Inside Miniconf – Pune

Machine Learning, Deep Learning and Artificial Intelligence: concepts, applications and tools.

DATE

24 November, Pune

STATUS

Accepting votes


About the event

When it comes to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI), three aspects are crucial:

  • Clarity of fundamental concepts.
  • Insights and nuances when applying concepts to solve real-world problems.
  • Knowledge of tools for automating ML and DL.

Anthill Inside Miniconf will provide understanding on each of these fronts.

Format

This miniconf is a full day event consisting of:

  1. 3-4 talks each, on concepts, applications and tools.
  2. Birds of Feather (BOF) sessions on focussed topics.

We are accepting proposals for:

  • 10 to 40-minute talks, explaining fundamnetal concepts in math, statistics and data science.
  • 20 to 40-minute talks on case studies and lessons learned when applyng ML, DL and AI concepts in different domains / to solve diverse data-related problems.
  • 10 to 20-minute talks on tools on ML and DL.
  • Birds of a Feather (BOF) sessions on failure stories in ML, to what problems / use cases should you use ML and DL, chatbots.
  • 3-6 hour hands-on workshops on concepts and tools.

Hands-on workshops

Hands-on workshops for 30-40 participants on 25 November will help in internalizing concepts, and practical aspects of working with tools.
Workshops will be announced shortly. Workshop tickets have to be purchased separately.

Target audience, and why you should attend this event

  1. ML engineers who want to learn about concepts in maths, stats and strengthen foundations.
  2. ML engineers wanting to learn from experiences and insights of others.
  3. Senior architects and decision-makers who want to quick run-through of concepts, implementation case studies, and overview of tools.
  4. Masters and doctoral candidates who want to bridge the gap between academia and practice.

Selection process

Proposals will be shortlisted and reviewed by an editorial team consisting of practitioners from the community. Make sure your abstract contains the following information:

  1. Key insights you will present, or takeaways for the audience.
  2. Overall flow of the content.

You must submit links to videos of talks you have delivered in the past, or record and upload a two-min self-recorded video explaining what your talk is about, and why is it relevant for this event.

Also consider submitting links to the following along with your proposal:

  1. A detailed outline, or
  2. Mindmap, explaining the structure of the talk, or
  3. Draft slides.

Honorarium for selected speakers; travel grants

Selected speakers and workshop instructors will receive an honorarium of Rs. 3,000 each, at the end of their talk. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants are available for domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans.
If you require a grant, mention this in the field where you add your location. Anthill Inside Miniconf is funded through ticket purchases and sponsorships; travel grant budgets vary.

Important dates

Anthill Inside Miniconf – 24 November, 2017.
Hands-on workshops – 25 November, 2017.

Contact details:

For more information about speaking, Anthill Inside, sponsorships, tickets, or any other information contact support@hasgeek.com or call 7676332020.


Confirmed sessions

Applied Machine Learning for realtime #FairPlay against Fraud

Aditya Prasad Narisetty (@adityaprasadn)

  • Flash talks
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Mon, 20 Nov
  • slideshow

Build intelligent, real-time applications using Machine Learning

Jayesh Sidhwani (@jayeshsidhwani)

  • Full talk
  • Intermediate
  • 8 upvotes
  • 0 comments
  • Tue, 14 Nov
  • slideshow

Doing Data Science on Cloud

Swapnil Dubey (@swapnildubey)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Mon, 13 Nov
  • slideshow

How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.

Shourya Roy (@shourya)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Sun, 12 Nov

Applying ML in AdTech and Lifecycle of an ML project

Satish Gopalani (@satishg)

  • Full talk
  • Beginner
  • 4 upvotes
  • 0 comments
  • Fri, 10 Nov
  • slideshow

Analytics without paralysis!

Ajay Kelkar

  • Crisp Talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Fri, 10 Nov

Machine Learning in Molecular Biology

Leelavati Narlikar (@leelavati)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Thu, 9 Nov
  • slideshow

Leapfrog in Deep Learning

Sameer Mahajan (@sameermahajan)

  • Workshop
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Fri, 3 Nov
  • slideshow

Inference in Deep Neural Networks

saurabh agarwal (@saurabh-agl)

  • Full talk
  • Intermediate
  • 4 upvotes
  • 0 comments
  • Wed, 1 Nov
  • play_arrow
  • slideshow

(Not so) Straight (!) fun with Linear Regression

Vishal (@vishalgokhale)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Fri, 27 Oct
  • slideshow

Fundamental Math Concepts for Data Science / ML / AI

Vishal (@vishalgokhale)

  • Workshop
  • Beginner
  • 1 upvotes
  • 0 comments
  • Fri, 27 Oct

Bayesian methods in data analysis, an introduction

Harshad Saykhedkar (@harshss)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Thu, 12 Oct
  • slideshow

Getting started with machine learning: tools, algorithms and concepts

Harshad Saykhedkar (@harshss)

  • Workshop
  • Beginner
  • 1 upvotes
  • 0 comments
  • Thu, 12 Oct

Unconfirmed proposals

Image Classification using Support Vector Machines.

Swapnil Dubey (@swapnildubey)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Mon, 13 Nov

How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.

Shourya Roy (@shourya)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Sun, 12 Nov

How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.

Shourya Roy (@shourya)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Sun, 12 Nov

Deep Reinforcement Learning: A hands-on approach

Shubham Dokania (@shubhamdokania)

  • Workshop
  • Intermediate
  • 19 upvotes
  • 0 comments
  • Wed, 8 Nov
  • slideshow