Machine Learning in Molecular Biology

Nov 2017

20 Mon

21 Tue

22 Wed

23 Thu

24 Fri 10:00 AM – 05:50 PM IST

25 Sat

26 Sun

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##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:

3-4 talks each, on concepts, applications and tools.
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

ML engineers who want to learn about concepts in maths, stats and strengthen foundations.
ML engineers wanting to learn from experiences and insights of others.
Senior architects and decision-makers who want to quick run-through of concepts, implementation case studies, and overview of tools.
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:

Key insights you will present, or takeaways for the audience.
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:

A detailed outline, or
Mindmap, explaining the structure of the talk, or
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

Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more

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This submission has been added to the schedule

Machine Learning in Molecular Biology

Submitted Nov 9, 2017

Section: Full talk Technical level: Beginner

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 Bayesian principles can come to the rescue. I will also talk about the importance of “feature selection” in both paradigms of learning: supervised as well as unsupervised.

Outline

Brush up on high-school biology. (3mins)
Introduction to some of the new biotechnologies that produce data. (2mins)
The Biological problems we are trying to solve. (5mins)
Mixture models and why feature selection is important in an unsupervised learning kind of a setting, with an example.(10mins)
An example of a Biological problem than can be formulated as supervised learning.(10mins)
5a. Some pictures of genetically modified creatures from our collaborators (that show machine learning works!).

Speaker bio

I am part of a group of scientists at the National Chemical Laboratory, Pune, who use mathematics and computation to understand diverse aspects of Biology. I am a computer scientist by training and work primarily on designing probabilistic models as well as algorithms to learn them, all with the hope of solving fundamental problems in genomics.