Breaking the Black-box - Githubissues

ghost commented 4 years ago

Title of the talk/workshop Breaking the Black-box

Abstract of the talk/workshop With the more complex algorithms like deep neural networks, random forest with 1000s of trees or dense machine learning models we are achieving the desired accuracy with a sacrifice of interpretability. If we are more interested in interpretability, we are sacrificing accuracy. In domains like finance or banking both are needed in justifying a prediction which helps the client and customers to understand why it predicted in that way. so how do we build interpretable machine learning models or explainable artificial intelligence? In this workshop, I will be explaining why it is important to build Interpretables models and how to draw insights from it and how to trust your model and make human to understand them, with the help of available methods.

Outline

Importance of interpretable machine learning: Understanding Machine Learning Model Interpretation, Importance of Machine Learning Model Interpretation, Criteria for Model Interpretation Methods, Scope of Model Interpretation
Model interpretation techniques: Accuracy vs. Interpretability trade-off, Model Interpretation Techniques
Hands-on: We will be using the latest state-of-the-art model interpretation frameworks like ELI5, Skater, Lime and SHAP

Learning Outcomes:

Getting familiar with frameworks like ELI5, LIME and SHAP
Getting familiar with concepts like feature importance, partial dependence plots, interpretability and explanation

Duration (including Q&A) 1 hour

Level of Audience Begineer/Intermediate

Speaker Bio

I love to give back to the community, with that I started to do blogging about Machine learning on Instagram which has around 80K followers. I'm working as an ML intern in synopsys as a full time and also as a research associate for MUST research academy as part-time.
Company - Synopsys
Email - udaykiran.kondreddy@gmail.com
Years of Exp - 1 year

Prerequisites(if any)

Basic understanding of machine learning and model building
Familiarity with Python