Submissions

Anthill Inside Miniconf – Pune

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

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.

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Accepting submissions

Not accepting submissions

Harshad Saykhedkar

Getting started with machine learning: tools, algorithms and concepts

This workshop will serve as a starting point for beginners in machine learning. I will cover a high level overview of field of machine learning and introduction to the Python data ecosystem in machine learning. I strongly believe that the best way to learn machine learning is by building few algorithms from scratch. So we will build a supervised ML application from scratch in Python. Since ML is … more
  • 0 comments
  • Confirmed
  • 12 Oct 2017
Workshop Beginner

Harshad Saykhedkar

Bayesian methods in data analysis, an introduction

If you are in a sector where the outcome of your data analysis and machine learning work has significant monetory impact, then you should learn bayesian data analysis! more
  • 0 comments
  • Confirmed & scheduled
  • 12 Oct 2017
Full talk Beginner

Vishal

Fundamental Math Concepts for Data Science / ML / AI

Many beginners intrigued by Data Science/ML/AI behold it in the awe and fear reserved for a hairy monster, A lot of really interested, good prorammers seem to maintain distance from it because they are just plain scared of the math. The workshop will be a refresher of the basic concepts and does not assume any prior knowledge greater than addition, subtraction, multiplication and division. more
  • 0 comments
  • Confirmed
  • 28 Oct 2017
Workshop Beginner

Vishal

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

We’ll conduct a live experiment with the help of volunteers and then analyze the data collected using linear regression. more
  • 0 comments
  • Confirmed & scheduled
  • 28 Oct 2017
Full talk Beginner

saurabh agarwal

Video thumbnail

Inference in Deep Neural Networks

A lot of focus is currently on training neural networks and better architecture. But we don’t focus alot on inference because well we are busy making our models work. Inference is supposed to run millions of time more than training and alot of times the inference is supposed to run on embeded devices. This talk will go into details of how the advancements in hardware have made Deep Learning possi… more
  • 0 comments
  • Confirmed & scheduled
  • 01 Nov 2017
Full talk Intermediate

Sameer Mahajan

Leapfrog in Deep Learning

Machine learning (ML) gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, ML explores the study and construction of algorithms that can learn from and make predictions on data through building a model from sample inputs. It’s a really exciting & impactful phase in the … more
  • 0 comments
  • Confirmed
  • 03 Nov 2017
Workshop Intermediate

Shubham Dokania

Deep Reinforcement Learning: A hands-on approach

Deep Reinforcement Learning has been becoming very popular since the dawn of DeepMind’s AlphaGo and DQN. Algorithms that learn to solve a game (sometimes better than) humans seems very complex from a distance, and we shall unravel the mathematical workings of such models through simple processes. This workshops aims to provide a simple insight about Reinforcement Learning and going to Deep RL. more
  • 0 comments
  • Submitted
  • 08 Nov 2017
Workshop Intermediate

Leelavati Narlikar

Machine Learning in Molecular Biology

Why do we need new machine learning algorithms to solve problems in molecular biology? Most “plug and play” packages cannot be applied directly, because often it is not even clear how to pose the problem as one of machine learning. Also, high-throughput biotechnologies keep evolving, producing different “types” of data, so the methods have to keep up. I will show how probabilistic models based on… more
  • 0 comments
  • Confirmed & scheduled
  • 09 Nov 2017
Full talk Beginner

Ajay Kelkar

Analytics without paralysis!

Storytelling with Data is becoming much more common today because of both vast amounts of data being available in the public space & also the emergence of a newer breed of younger, more “social” professionals who consume such data with far more ease! AI & machine learning are also changing the context within which you can tel data stories.In this talk I will look at examples of how data insights … more
  • 0 comments
  • Confirmed & scheduled
  • 10 Nov 2017
Crisp Talk Beginner

Satish Gopalani

Applying ML in AdTech and Lifecycle of an ML project

Description: This talk will provides insights into how ML is being applied to solve real world problems in AdTech and at scale in PubMatic. It will also cover the entire lifecycle of a typical Machine Learning Project. more
  • 0 comments
  • Confirmed & scheduled
  • 10 Nov 2017
Full talk Beginner

Shourya Roy

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

I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
  • 0 comments
  • Cancelled
  • 12 Nov 2017
Full talk Intermediate

Shourya Roy

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

I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
  • 0 comments
  • Confirmed & scheduled
  • 12 Nov 2017
Full talk Intermediate

Shourya Roy

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

I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
  • 0 comments
  • Cancelled
  • 12 Nov 2017
Full talk Intermediate

Swapnil Dubey

Doing Data Science on Cloud

With the increase in data size for running DS models,it is important to look into possible infrastructure options which provide enough scalability to run DS algo successfully.Optimal use of infrastructure in terms of cost is the need of hour.For example,running task using multiple GPU for finite amount of time. Almost all the Cloud vendors(AWS,Google,Microsoft) provide different kind of services … more
  • 0 comments
  • Confirmed & scheduled
  • 14 Nov 2017
Full talk Intermediate

Swapnil Dubey

Image Classification using Support Vector Machines.

In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.In this talk we will be looking at the basic fundamentals and implementation of SVM for image classification. more
  • 0 comments
  • Submitted
  • 14 Nov 2017
Full talk Beginner

Jayesh Sidhwani

Build intelligent, real-time applications using Machine Learning

The surge in the availability of large datasets, processing powers and the ability to process the data in real-time has opened up a plethora of opportunities in which Machine Learning algorithms can harness this power to build intelligent, real-time applications. more
  • 0 comments
  • Confirmed & scheduled
  • 14 Nov 2017
Full talk Intermediate

Aditya Prasad Narisetty

Applied Machine Learning for realtime #FairPlay against Fraud

For any firm processing online transactions, ensuring a strong shield against fraud is of top priority. And for platforms hosting fantasy sports and online gaming, ensuring a fair play from all users and real-time fraud detection is a first line of defence. Traditionally, rule based engines formed the crux of anomaly and fraud detection. But maintaining a rule engine and adapting to new patterns … more
  • 0 comments
  • Confirmed & scheduled
  • 20 Nov 2017
Flash talks Intermediate

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more