Lectures related to Explainable artificial intelligence

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Projection Pursuit Regression: Nonlinear Modeling and Interpretability

Explores Projection Pursuit Regression for nonlinear modeling and the trade-offs with interpretability in neural networks.

Deep Visual Recognition: Interpretability

Explores deep visual recognition, interpretability, CNN architectures, visual dictionaries, and attention mechanisms.

Challenges in DL/ML Applications

Explores challenges in deep learning and machine learning applications, covering surveillance, privacy, manipulation, fairness, interpretability, energy efficiency, cost, and generalization.

Deep Learning Paradigm

Explores the deep learning paradigm, including challenges, neural networks, robustness, fairness, interpretability, and energy efficiency.

Transformer Networks: Self-Attention

Explains Transformer networks and self-attention layers for mapping inputs and multi-head attention.