Machine Learning

Mathematics, once the primary driver of machine learning research, is shifting from providing theoretical guarantees and guiding granular architectural design to offering post-hoc explanations for empirical phenomena and…

Arthur Samuel's 1959 definition of machine learning as giving computers the ability to learn without explicit programming is explained, contrasting classic algorithms with ML approaches that learn from data. The spam fil…

A guide from an unnamed data science source lists seven essential machine learning algorithms that remain relevant despite the rise of large language models and generative AI, arguing that simpler models often solve prob…

The article explains that machine learning is a branch of artificial intelligence where computers learn patterns from data rather than following explicitly programmed rules. It introduces three main types of machine lear…

A beginner's guide on Towards AI explains ensemble learning methods bagging and boosting, covering decision trees, Random Forest, and XGBoost with analogies, examples, and code.

A University of Lausanne study found that an explainable machine learning pipeline assessing mental health risks in students produced stable results that reflected data construction rather than actual outcomes. When trai…

The first lecture in a series on AI and machine learning for clinicians covers the fundamentals of classical machine learning, including supervised, unsupervised, and reinforcement learning, model lifecycles, gradient de…

A machine learning framework using the IBM Telco Customer Churn dataset achieved 77.68% accuracy and a ROC AUC of 0.8403 in predicting customer churn, with CatBoost as the top performer among three gradient boosting ense…

A developer provides a hands-on introduction to AI and machine learning for developers, focusing on building a simple linear regression model using Python, NumPy, and scikit-learn. The tutorial covers generating syntheti…

Researchers have developed a physics-constrained machine learning framework that accelerates turbulent reacting flow simulations by more than ten times without sacrificing accuracy. The framework enforces the second law …

A machine learning model built from scratch on the UCI Adult Census Income dataset reaches 84 percent accuracy, but the project's real value lies in demonstrating overfitting and underfitting. A deep decision tree with m…

A machine learning researcher pitted 21 algorithms against each other in a regression task using a 512x512 image as the target function, but artificially inflated the input space to 500, 1000, and 2000 features, with onl…

Researchers used machine learning to rediscover equations governing ocean biogeochemistry, specifically colloidal iron cycling, from sparse data. The technique, symbolic regression, produced simpler equations that perfor…

SCALA, a new machine learning framework inspired by cognitive psychology, improves model accuracy and sample efficiency in data-scarce environments by guiding learning from broad categories to fine-grained recognition. T…

A new machine learning optimization framework focuses on five real-world constraints — data availability, latency budget, memory budget, accuracy tolerance, and retraining budget — to guide deployment decisions beyond tr…

The Complete Machine Learning Bundle, featuring over 10 courses on topics including self-driving cars and speech recognition, is on sale for $30 through the Techdirt Deals Store, powered by StackCommerce.

A new paper on arXiv (submitted July 23, 2026) provides an overview of Bayesian and frequentist simulation-based inference (SBI) with machine learning, covering methods such as neural posterior estimation and neural like…

Researchers used machine learning models to predict exam outcomes from physiological data such as electrodermal activity, heart rate, and skin temperature. Deep learning models like transformers and LSTMs captured comple…

A new study using four years of data from Belgian offshore wind farms shows that tree-based machine learning models, particularly conditional diffusion models, improve wind power forecast accuracy by 5% in mean absolute …

Machine learning detects fraud by combining supervised models trained on past fraudulent transactions with unsupervised anomaly-detection methods that catch novel fraud patterns. Production fraud systems use both approac…