Interpret-ability as a bridge from Insights to Intuition in Machine and Deep Learning

Nov 2019

18 Mon

19 Tue

20 Wed

21 Thu

22 Fri

23 Sat 08:30 AM – 05:30 PM IST

24 Sun

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Accepting submissions till 01 Nov 2019, 04:20 PM

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##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 sales@hasgeek.com

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Interpret-ability as a bridge from Insights to Intuition in Machine and Deep Learning

Submitted Apr 30, 2019

Section: Full talk Technical level: Intermediate Session type: Lecture

Requisite characteristics of the Machine Learning models that make them fully deployable in a business setting are multivarious and sometimes compelling. Mere predictive power and validation accuracy are sometimes not sufficient. These models need to be interpretable, bias-free, transparent, explainable, consistent and should have a global and local basis for their output predictions. In this talk we would address various contemporary state of the art approaches to explain and interpret the complex models, ranging from linear and logistic models to Deep Learning models. We will focus on fundamental Model Interpretability principles to build simple to complex models. We will cover a mix of Model specific and Model Agnostic Interpretation strategies. Some of the models that would be covered are; Decision Tree Surrogate models, ICE plots, K-LIME, LOCO, Partial dependent plots and Random Forest Feature importance. Two great model interpretation strategies LIME and SHAP would be introduced and covered in depth. You will learn the process of applying and interpreting these techniques on real-world datasets and casestudies.

Outline

Machine Learning Interpretability - Foundational Principles
Model Agnostic and Model specifc Tools for interpretability, and, why we need both the flavors
Interpreting simple to complex models (linear/logistic to Deep Learning Models like Convolutional Neural Nets)
Notations, insights and key ideas related to techniques such as K-LIME, SHAP, ICE plots, PD plots & LOCO.
Insights from interpretation, appropriateness of model updates, bias/transparency handling etc.
Examples/Case Studies, insights for production implementation