Social And Emotional Learning Courses

Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021

Explore adversarial examples in machine learning, their impact on linear and non-linear models, and techniques like adversarial training to improve model robustness and generalization.

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

Introduction to Adversarial Attacks in Machine Learning - Lecture 1

Introduction to adversarial attacks on AI systems, covering real-world examples, attack methods, and defense strategies for various applications like face recognition and object detection.

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

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Explore techniques for interpreting and explaining deep learning models, focusing on LRP rules, Taylor decomposition, and properties of effective explanations in AI systems.

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

Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021

Unmasking clever predictors: Explore Layer-wise Relevance Propagation and SPRAY techniques to assess machine learning models' decision-making processes and identify potential biases or faults.

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

Robust Pre-Training by Adversarial Contrastive Learning - CAP6412 Spring 2021

Explore robust pre-training techniques using adversarial contrastive learning, focusing on unsupervised and semi-supervised methods for improved model resilience and accuracy.

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

Adversarial Self-Supervised Contrastive Learning

Explore adversarial self-supervised contrastive learning, covering transformers, frameworks, algorithms, evaluations, and experiments in this comprehensive presentation.

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

Contrastive Learning with Adversarial Examples - Spring 2021

Explore contrastive learning with adversarial examples, covering unsupervised embedding, self-supervised learning, experiments, and architectural studies in this comprehensive overview.

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

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Explore effective strategies for handling imbalanced datasets using label-distribution-aware margin loss, improving machine learning model performance on skewed data.

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

Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction

Explore unsupervised keypoint learning techniques for guiding class-conditional video prediction, enhancing the accuracy and efficiency of video generation models.

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

Consistency-Based Semi-Supervised Learning for Object Detection

Explore semi-supervised learning for object detection, focusing on consistency-based approaches. Learn about loss functions, background elimination, and experimental results in this concise presentation.

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

Unsupervised Meta-Learning for Few-Shot Image Classification

Explore unsupervised meta-learning techniques for few-shot image classification, including task construction, augmentation methods, and performance on benchmark datasets.

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

Learning to See in the Dark - Extreme Low Light Imaging Techniques

Explore extreme low-light imaging techniques using deep learning, covering dataset creation, network architecture, and experimental results for improved night vision photography.

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

Learning to Navigate in Cities Without a Map

Explore reinforcement learning techniques for urban navigation without maps, focusing on value functions, courier tasks, and multi-site experiments.

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

Taskonomy - Disentangling Task Transfer Learning

Explore task transfer learning, higher-order transfers, and task-specific networks. Discover global taxonomies, evaluate computed structures, and assess generalization to novel tasks.

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

Understanding Deep Learning Requires Rethinking Generalization

Explore deep learning generalization, examining regularization techniques, randomization tests, and neural network expressivity to challenge conventional understanding and improve model performance.

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