Social And Emotional Learning Courses

Domain Adaptation with Invariant Representation Learning - What Transformations to Learn?

Explore domain adaptation in machine learning, focusing on invariant representation learning and its challenges. Learn about novel techniques for optimal feature mapping across domains.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Deep Learning Methods for Electrocardiograms and Echocardiograms

Explore cutting-edge deep learning methods for ECG and echocardiogram analysis, outperforming traditional algorithms and offering insights into cardiovascular imaging interpretation.

• YouTube
• 1 hour 1 minute
• Self-Paced
• Free Video

Adversarial Debiasing With Partial Learning - Medical Image Studies

Explore adversarial debiasing techniques to reduce racial disparities in medical AI models for chest X-rays and mammograms while maintaining overall performance and trustworthiness.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Towards Generalist Imaging Using Multimodal Self-Supervised Learning - Mars Huang

Exploring multimodal self-supervised learning for generalist medical imaging AI, addressing limitations of supervised learning and clinical context integration in automated image analysis.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Learning the Structure of EHR with Graph Convolutional Transformer - Edward Choi

Explore graph convolutional transformers for learning EHR structure, combining graph convolution and self-attention for supervised prediction tasks in healthcare analytics and representation learning.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

How Can You Trust Machine Learning?

Explore the pillars of trust in machine learning: clarity, competence, and alignment. Learn about processes and techniques to develop more effective and trustworthy AI systems.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Joint Learning Over Visual and Geometric Data

Explore joint learning approaches in visual and geometric domains to reduce supervision load, leveraging data correlations and self-supervision for improved performance and efficiency.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

How Machine Learning is Changing Software

Explore how machine learning is revolutionizing software development, from weak supervision to self-supervised systems, with insights from Stanford's Chris Re on emerging challenges and industry applications.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Learning to See the Physical World

Explore physical scene understanding through AI, combining computer graphics, physics, and language with deep learning to create versatile machines that see, reason, and interact with the world.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Robots in Dynamic Tasks - Learning, Risk, and Safety

Explore advanced robotics in dynamic environments, covering autonomous drones, aerial manipulators, and risk-aware planning. Learn about machine learning applications, nonlinear control, and adaptive strategies for complex robotic tasks.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Stanford Seminar - Democratizing Robot Learning

Exploring advancements in human-robot interaction, focusing on learning from human decision-makers, explainable AI, and coordinated control of human-robot teams for real-world applications.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning

Explore reverse engineering of transformer language models, focusing on induction head circuits and their role in in-context learning. Gain insights into neural network interpretability and AI safety.

• YouTube
• 1 hour
• Self-Paced
• Free Video

How Can You Trust Machine Learning?

Explore a framework for trustworthy AI based on Clarity, Competence, and Alignment. Learn about explainable ML, rigorous model evaluation, and aligning AI behaviors with human values.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Interactive Imitation Learning: Planning Alongside Humans

Interactive imitation learning for robots: tackling feedback-driven covariate shift and predicting human intent using graph neural networks, with applications in self-driving vehicles.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Hacking AI - Security & Privacy of Machine Learning Models

Explore adversarial machine learning with Prof. Dan Boneh, examining security challenges in AI models, defenses against attacks, and the balance between accuracy and privacy in ML systems.

• YouTube
• 58 minutes
• Self-Paced
• Free Video