Explainable Al (XAI) with Python
What you'll learn
- Importance of XAI in modern world
- Differentiation of glass box, white box and black box ML models
- Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques
- Trade-off between accuracy and interpretability
- Application of InterpretML package from Microsoft to generate explanations of ML models
- Need of counterfactual and contrastive explanations
- Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss
- Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.
- What-if tool from Google to analyze data points and to generate counterfactuals
- No programming experience needed. You will learn everything you need to know to apply XAI for generating explanations for ML models.
XAI with Python
This course provides detailed insights into the latest developments in Explainable Artificial Intelligence (XAI). Our reliance on artificial intelligence models is increasing day by day, and it's also becoming equally important to explain how and why AI makes a particular decision. Recent laws have also caused the urgency about explaining and defending the decisions made by AI systems. This course discusses tools and techniques using Python to visualize, explain, and build trustworthy AI systems.
This course covers the working principle and mathematical modeling of LIME (Local Interpretable Model Agnostic Explanations), SHAP (SHapley Additive exPlanations) for generating local and global explanations. It discusses the need for counterfactual and contrastive explanations, the working principle, and mathematical modeling of various techniques like Diverse Counterfactual Explanations (DiCE) for generating actionable counterfactuals.
The concept of AI fairness and generating visual explanations are covered through Google's What-If Tool (WIT). This course covers the LRP (Layer-wise Relevance Propagation) technique for generating explanations for neural networks.
In this course, you will learn about tools and techniques using Python to visualize, explain, and build trustworthy AI systems. The course covers various case studies to emphasize the importance of explainable techniques in critical application domains.
All the techniques are explained through hands-on sessions so that learns can clearly understand the code and can apply it comfortably to their AI models. The dataset and code used in implementing various XAI techniques are provided to the learners for their practice.
Who this course is for:
- Students taking Machine Learning Course or Artificial Intelligence Course
- Students who are looking to make career in AI
- Beginner Python programmers who already have some foundational knowledge with machine learning libraries.
- Researchers who already use Python for building AI models and can benefit from learning the latest explainable AI techniques to generate explanations of their models
- Data analysts and data scientists that want an introduction to explainable AI tools and techniques using Python for machine learning models.
Parteek Bhatia is Professor in the Department of Computer Science and Engineering and Former Associate Dean of Student Affairs at Thapar Institute of Engineering and Technology, Patiala. At present he is on sabbatical at Tel Aviv University, Israel and acting as Visiting Professor at LAMBDA Lab, TAU. He is recipient of Young Faculty Research Fellowship from Ministry of Electronics & Information Technology, Govt. of India.
He has more than twenty years of academic experience. He has completed his Ph.D on "UNL Based Machine Translation System for Punjabi Language" from Thapar University. He has published more than 75 research papers and articles in Journals, Conferences and Magazines of repute. His research work with UNDL foundation, Geneva, Switzerland involved participation in Advanced UNL School at Alexandria, EGYPT in 2012 and at Geneva, Switzerland in 2013 and 2014. He is a winner of Gold Medal at International competition UNL Olympiad II,UNL Olympiad III and UNL Olympiad IV conducted by UNDL Foundation in year 2013 and 2014.
He has authored multiple text books including Data Mining and Data Warehousing: Principles and Practices from Cambridge University press, Simplified Approach to DBMS, Simplified Approach to Visual Basic and Simplified Approach to Oracle. He is acting as Principal Investigator on several projects funded by Government of India.