Interpretability Courses and Certifications

Simons Institute

Interpretability via Symbolic Distillation

Explore symbolic distillation techniques for enhancing interpretability in large language models and transformers, focusing on practical applications and theoretical insights.

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

Simons Institute

Towards Interpretable Data Science

Explore holistic approaches to interpretable data science, focusing on counterfactual and attribution-based explanations for complex data processing and decision support systems.

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

Open Data Science

Measuring Interpretability in AI - Transforming Black Box Systems into Transparent Tools

Explore novel metrics for measuring AI interpretability in unsupervised and supervised methods, enhancing transparency in topic models and question-answering systems for broader applications.

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

Simons Institute

Interpretability Agents: Automating and Scaling Model Interpretation

Explore automated interpretability agents for scaling model interpretation, enabling efficient analysis of AI systems through hypothesis-driven experimentation and modular design.

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

USC Information Sciences Institute

Pragmatic Interpretability - A Human-AI Cooperation Approach

Explore pragmatic interpretability in machine learning, focusing on human-AI cooperation, selective explanations, and incorporating human intuition to enhance decision-making and model understanding.

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

Toronto Machine Learning Series (TMLS)

Interpretability Tools as Feedback Loops in Machine Learning Training

Explore machine learning interpretability techniques to optimize training sequences and develop more effective models without increasing compute or training time.

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

ACM SIGPLAN

Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks

Explore a neurosymbolic framework for improving CNN interpretability in image classification using logic programming and kernel-grouping techniques.

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

Valence Labs

Uncovering and Inducing Interpretable Causal Structure in Deep Learning Models

Explore causal abstraction theory for uncovering and inducing interpretable structures in deep learning models, focusing on faithful explanations and interventions during analysis and training.

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

Explaining Model Decisions and Fixing Them Through Human Feedback

Explore algorithms for explaining deep network decisions, building user trust, incorporating domain knowledge, learning grounded representations, and correcting unwanted biases in AI models.

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

Optimizing for Interpretability in Deep Neural Networks - Mike Wu

Explore techniques for making deep neural networks more interpretable through regularization, focusing on medical applications in critical care and HIV treatment. Insights on balancing model performance with human-understandable decision processes.

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

Leveraging Social Theories to Enhance Human-AI Interaction

Explores challenges in human-AI partnerships, focusing on interpretability tools' limitations and cognitive heuristics. Proposes incorporating social theories to improve decision-support systems and human-AI interaction.

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

Developing Explainable AI (XAI)

Gain skills to develop transparent, trustworthy AI systems using Explainable AI techniques. Learn key concepts, evaluate approaches, and apply XAI to emerging trends like Generative AI.

• Coursera
• 8 hours 26 minutes
• On-Demand
• Free Online Course (Audit)

Microcredential

Responsible AI for Developers

Equip yourself with essential skills to build ethical AI systems. Learn to mitigate bias, interpret models, prioritize privacy, and ensure safety in AI development using Google Cloud tools.

• Coursera
• On-Demand
• Paid Course
• 3 courses

Responsible & Safe AI Systems

Explore ethical, social, and safety aspects of AI systems. Learn about algorithmic transparency, fairness, interpretability, and potential risks. Develop critical thinking skills for responsible AI development and deployment.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Interpretable Explanations of Black Boxes by Meaningful Perturbation - CAP6412 Spring 2021

Explore principles and methods for explaining black box functions in machine learning, focusing on image saliency, perturbation techniques, and meta-predictors for interpretable AI.

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