Machine Learning for Threat Classification: Addressing Class Imbalance and Explainability
Explore techniques for class imbalance, explainability, and best practices in machine learning for threat classification modeling.
Machine Learning
Explore machine learning models through hands-on tutorials in Python and Julia, covering clustering, regression, neural networks, and more. Practical examples with code provided.
Predicting Rare Events Using Ordinal Regression - From Purchases to Diseases
Dive into advanced ordinal regression techniques for predicting rare events like purchases and diseases, with practical examples and implementation guidance.
Simple Training with the Transformers Trainer
Learn to train and fine-tune Transformer models using the Trainer API, covering tokenization, class balance, and practical implementation techniques.
Class Imbalance and Classification Metrics With Aircraft Wildlife Strikes
Explore how handling class imbalance affects classification metrics using aircraft wildlife strike data, with focus on sensitivity and specificity in predictive modeling.
Trustworthy Deep Learning
Explore robust and trustworthy deep learning, covering algorithmic bias, uncertainty estimation, and innovative approaches to create risk-aware AI systems for more reliable and ethical machine learning applications.
Build a Predictive Text Model for Avatar: The Last Airbender with Tidymodels
Predict Avatar: The Last Airbender speakers using text features and tidymodels in R. Learn model-agnostic variable importance and handle class imbalance in this data analysis tutorial.
Robustness to Adversarial Inputs and Tail Risk via Boosting - Machine Learning Model Defense Strategies
Explore innovative strategies for robust machine learning models against adversarial inputs and tail risks, focusing on ensemble methods and risk-sensitive decision-making in high-stakes environments.
The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluation
Explore machine learning challenges in side-channel analysis, focusing on class imbalance and conflicting metrics. Learn strategies to address these issues in cryptographic hardware evaluation.
AI Bias and Fairness
Comprehensive exploration of AI bias and fairness, covering types, sources, and mitigation strategies. Examines interpretation-driven and data-driven biases, feature bias, and advanced debiasing techniques for ethical AI development.