Concept

Interpretability

Related lectures (24)

Deep Visual Recognition: Interpretability

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

Machine Learning for Medicine: Insights and Interpretability

Explores the application of machine learning in medicine, emphasizing interpretability, variability between patients, and the quest for transparent equations in medical models.

Explainability: Methods and Applications

Introduces interpretability methods like PDP and LIME for understanding model predictions.

Generative Modeling: Then, Now and Tomorrow?

Explores the evolution of generative modeling, from traditional methods to cutting-edge advancements, addressing challenges and envisioning future possibilities.

Mathematics of Data: Deep Learning Introduction

Covers a mathematical introduction to deep learning, including challenges, power of linear classifiers, model scaling, and theoretical aspects.

Model Analysis

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Explainable Neural Networks

Explores the concept of explainable neural networks and their significance in improving model interpretability, particularly in finance and house price valuation.

Autonomous Vehicles: Trajectory Prediction and Social Behavior

Explores challenges in deep learning for autonomous vehicles, emphasizing social behavior modeling and feasible trajectory prediction.

Classification: Introduction

Covers clustering and classification, building models to assign objects to classes based on attribute values.

Geometric Insights on Deep Learning Models

Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.

Projection Pursuit Regression: Nonlinear Modeling and Interpretability

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

Deep Learning for Autonomous Vehicles

Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.

Artificial Intelligence in Finance

Delves into the application of artificial intelligence in finance, exploring tools like neural networks and Bayesian techniques, successful use cases in fraud detection and robo-advisors, and the importance of interpretability in machine learning models.

Transformer Networks: Self-Attention

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

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Deep Learning Paradigm

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

Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Adversarial Training: Optimization and Applications

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Variance Reduction Techniques

Covers variance reduction techniques in optimization, focusing on gradient descent and stochastic gradient descent methods.