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Concept

Explainable artificial intelligence

Related lectures (27)

Explainability: Methods and Applications

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

Explainable Neural Networks

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

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Machine Learning on Earth System with Remote Sensing

Explores machine learning applications in Earth system analysis using remote sensing data, focusing on automatic image interpretation and explainable AI.

Sparse Communication: Transformations and Applications

Explores the evolution from sparse modeling to sparse communication in neural networks for natural language processing tasks.

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 for Question Answering

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

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.

Ethical Issues in AI

Explores the ethical challenges of artificial intelligence, including explainability, security, singularity, and societal impact.

Mathematics of Data: Deep Learning Introduction

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

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

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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.

Machine Learning for Medicine: Insights and Interpretability Log in to Mediaspace to watch this video

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

Scalable and Trustworthy Learning: Challenges and Solutions Log in to Mediaspace to watch this video

Explores challenges and solutions for scalable and trustworthy learning in heterogeneous networks, emphasizing data heterogeneity, privacy, fairness, and robustness.