About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
Feature selection and engineering using genetic algorithms and genetic programming
While feature selection is almost a solved problem in data science, feature engineering is still quite a mystery. In this talk I will outline a method that I use to solve feature engineering, with a goal to provide a generalized framework to tackle both feature engineering and selection simultaneoously.
The first few slides will talk about the application of genetic algorithms (GA) to feature selection. The next couple of slides will talk about advancements made to GAs by use of a multi-dimensional covariance map, a method that I developed. The next couple of slides will talk about genetic programming (GP) and how one can use the multi-dimensional covariance map to augment the convergence of GPs.
A good understanding of machine learning fundamentals
I’m currently a principal data scientist at Intuit. A public but slightly dated bio is available here: https://www.analyticsvidhya.com/datahack-summit-2018/speakers/sidharth-kumar/ An informal writeup on me is available here: http://humansofanalytics.com/stories/sidharth-kumar-data-science-savant-machine-learning-aficionado-and-ardent-chess-player/