CORE

Explainable AI

24CSE821 · S8 · 4 credits

Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.

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Modules
Module 01Overview of AI interpretability and explainabilityexpand

Introduction: Interpretability-importance,scope, Taxonomy of Interpretability Methods, evolution, Properties of Explanations-Human friendly Explanations. Importance of XAI in real-world applications Ethical considerations in AI and the need for transparency

Module 02Inherent Model Transparencyexpand

Inherent Model Transparency: Decision Trees and Rule-based Models Rule-based Models: Association rule mining, Sequential rule mining, Rule-based systems in AI, Linear Models and Logistic Regression, Generalized Additive Models (GAM)

Module 03Interpretable Neural Networksexpand

Overview of neural network interpretability:-Saliency maps and gradient-based methods, Layer-wise relevance propagation (LRP) Time Series and Sequence Data Interpretability: Interpretable models for time series data, Sequence- to-sequence models and attention mechanisms

Module 04Explainable Reinforcement Learning and Model-Agnosticexpand

Methods Explainable Reinforcement Learning: Importance of explainability in reinforcement learning, State-of-the-art methods in explaining RL decisions Model-Agnostic Methods -introduction, LIME (Local Interpretable Model-agnostic Explanations) SHAP (SHapley Additive exPlanations), Anchors

Module 05XAI in Industry and Real-world Applicationsexpand

XAI in Industry and Real-world Applications: Case studies of XAI implementation in various industries, Challenges and success stories, Regulatory aspects and compliance Future Trends and Research Directions:- Emerging trends in XAI research Open challenges and opportunities

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