Foundations

ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

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ML Maths Basics

Manipulate vectors and matrices

Supervised Learning

Train decision trees, random forests, and neural nets

Unsupervised Learning

Apply k-means and DBSCAN clustering

Engineer features and handle missing data

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ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Mapping data literacy trajectories in K-12 education

arXiv:2603.28317v1 Announce Type: cross Abstract: Data literacy skills are fundamental in computer science education. However, understanding how data-driven sys

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data

arXiv:2603.28417v1 Announce Type: cross Abstract: This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

A Convex Route to Thermomechanics: Learning Internal Energy and Dissipation

arXiv:2603.28707v1 Announce Type: cross Abstract: We present a physics-based neural network framework for the discovery of constitutive models in fully coupled

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds

arXiv:2603.28764v1 Announce Type: cross Abstract: Similarity measures are widely used to interpret the representational geometries used by neural networks to so

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling

arXiv:2510.17211v2 Announce Type: replace Abstract: Disease progression modeling aims to characterize and predict how a patient's disease complications worsen o

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

arXiv:2307.07753v2 Announce Type: replace-cross Abstract: In this work, we propose a novel prior learning method for advancing generalization and uncertainty es

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems

arXiv:2501.00277v2 Announce Type: replace-cross Abstract: Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring

arXiv:2501.10677v3 Announce Type: replace-cross Abstract: The advent of artificial intelligence has significantly enhanced credit scoring technologies. Despite

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

A Benchmark for Incremental Micro-expression Recognition

arXiv:2501.19111v3 Announce Type: replace-cross Abstract: Micro-expression recognition plays a pivotal role in understanding hidden emotions and has application

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

arXiv:2503.09008v3 Announce Type: replace-cross Abstract: Long-range dependencies are critical for effective graph representation learning, yet most existing da

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Measuring the (Un)Faithfulness of Concept-Based Explanations

arXiv:2504.10833v4 Announce Type: replace-cross Abstract: Deep vision models perform input-output computations that are hard to interpret. Concept-based explana

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely $F_1$

arXiv:2511.22442v2 Announce Type: replace-cross Abstract: Ranking methods or models based on their performance is of prime importance but is tricky because perf

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Overcoming the Curvature Bottleneck in MeanFlow

arXiv:2511.23342v3 Announce Type: replace-cross Abstract: MeanFlow offers a promising framework for one-step generative modeling by directly learning a mean-vel

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval

arXiv:2512.04524v3 Announce Type: replace-cross Abstract: Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled targ

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Hellinger Multimodal Variational Autoencoders

arXiv:2601.06572v2 Announce Type: replace-cross Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning w

ArXiv cs.AI 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting

arXiv:2601.16632v3 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning. While prevailing approa

Towards Data Science 📐 ML Fundamentals ⚡ AI Lesson 3w ago

How to Lie with Statistics with your Robot Best Friend

What is p hacking, is it bad, and can you get ai to do it for you? The post How to Lie with Statistics with your Robot Best Friend appeared first on Towards Dat

5 Useful Python Scripts for Effective Feature Selection

KDnuggets 📐 ML Fundamentals ⚡ AI Lesson 3w ago

5 Useful Python Scripts for Effective Feature Selection

Learn five simple Python scripts to perform effective feature selection. Each one is practical, minimal, and easy to use in real projects.

7 Essential Python Itertools for Feature Engineering

Machine Learning Mastery 📐 ML Fundamentals ⚡ AI Lesson 3w ago

7 Essential Python Itertools for Feature Engineering

Feature engineering is where most of the real work in machine learning happens.