Trustworthy Machine Learning Courses and Certifications

Simons Institute

Privacy and Fairness in Collaborative AI

Explore information-theoretic approaches to enhance privacy and fairness in collaborative AI systems, focusing on trustworthy machine learning techniques.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Machine Learning Explainability Workshop

Dive into modern machine learning explainability techniques with Harvard's Prof. Hima Lakkaraju, covering interpretable models, post-hoc methods, and evaluation frameworks.

• YouTube
• 4 hours 12 minutes
• On-Demand
• Free Video

USENIX Enigma Conference

Trustworthy Machine Learning: Challenges and Frameworks

Exploring the security challenges in machine learning, this talk discusses strategies for building trustworthy AI systems by analyzing data influence, addressing vulnerabilities, and enhancing interpretability.

• YouTube
• 18 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning

Explore new theories and algorithms for complex dynamical systems, creating trustworthy AI for real-time prediction and national security applications. Learn about innovative neural networks and physics-integrated models.

• YouTube
• 49 minutes
• On-Demand
• Free Video

MLOps World: Machine Learning in Production

Is Your Machine Learning Model Trustworthy? Evaluating AI Quality and Reliability

Explore AI quality pillars, their importance, and evaluation methods to ensure trustworthy ML models. Learn automated techniques for assessing model performance, robustness, and explainability.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Simons Institute

Neurosymbolic Synthesis for Trustworthy Machine Learning

Explore neurosymbolic synthesis techniques for building trustworthy machine learning systems with Osbert Bastani from the University of Pennsylvania.

• YouTube
• 47 minutes
• On-Demand
• Free Video

IEEE Signal Processing Society

Trustworthy Machine Learning and Artificial Intelligence - Episode 6

Explore trustworthy AI principles with IBM Fellow Dr. Kush Varshney, covering fairness, explainability, governance, and signal processing techniques for accountable systems.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)

The Role of Machine Learning Copies in Ensuring a More Trustworthy AI

Explore how machine learning copies can enhance AI trustworthiness in this insightful talk from IFISC researchers.

• YouTube
• 1 hour 10 minutes
• On-Demand
• Free Video

Centre de recherches mathématiques - CRM

Statistical Frameworks for Trustworthy Machine Learning - Privacy, Uncertainty, and Online Inference

Discover advanced statistical frameworks for trustworthy ML, covering Gaussian differential privacy on manifolds and online quantile regression with uncertainty quantification.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Simons Institute

Information-Theoretic Foundations of Generative Adversarial Models

Explore information-theoretic methods for trustworthy machine learning, focusing on generative adversarial models and their foundations for enhanced reliability and performance.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Simons Institute

Generalization Bounds for Neural Network Based Decoders

Explore generalization bounds for neural network decoders, examining their implications for information-theoretic approaches in trustworthy machine learning.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Simons Institute

Optimal Neural Network Compressors and the Manifold Hypothesis

Explore the intersection of neural network compression and the manifold hypothesis, examining optimal compressors and their implications for machine learning efficiency and performance.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Simons Institute

Improving Accuracy-Privacy Tradeoff via Model Reprogramming

Explore innovative techniques for enhancing the balance between model accuracy and data privacy through advanced model reprogramming strategies.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Simons Institute

Fundamental Trade-Offs in FL-FA - Sparsity - DP - Communication Constraints

Explore trade-offs in federated learning, focusing on sparsity, differential privacy, and communication constraints. Gain insights into balancing these factors for effective machine learning.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Simons Institute

Contraction of Markov Kernels and Differential Privacy - Part I

Explore Markov kernel contraction and its connection to differential privacy in machine learning, focusing on information-theoretic approaches for trustworthy algorithms.

• YouTube
• 35 minutes
• On-Demand
• Free Video