Introduction Reinforcement Learning (RL) has seen explosive growth in recent years, powering breakthroughs in robotics, game playing, and autonomous control. While early successes focused on...

Introduction Reinforcement Learning (RL) has seen explosive growth in recent years, powering breakthroughs in robotics, game playing, and autonomous control. While early successes focused on...
Introduction Imagine a group of robots cleaning a warehouse, a swarm of drones surveying a disaster zone, or autonomous cars navigating through city traffic. In each of these scenarios, multiple...
Introduction Imagine trying to understand what someone said over a noisy phone call or deciphering a DNA sequence from partial biological data. In both cases, you're trying to uncover a hidden...
Introduction Reinforcement Learning (RL) is a powerful framework that enables agents to learn optimal behaviours through interaction with an environment. From mastering complex games like Go to...
Imagine teaching a robot to navigate a maze or training an AI to master a video game without ever giving it explicit instructions—only rewarding it when it does something right. This is the essence...
What is Federated Learning? Federated Learning (FL) is a cutting-edge machine learning approach emphasising privacy and decentralisation. Unlike traditional machine learning methods, which rely on...
In the age of digital transformation, Natural Language Processing (NLP) has emerged as a cornerstone of intelligent applications. From chatbots and voice assistants to real-time translation and...
What is Out-of-Distribution Detection? Out-of-Distribution (OOD) detection refers to identifying data that differs significantly from the distribution on which a machine learning model was trained,...
What is Anomaly Detection in LLMs? Anomaly detection in the context of Large Language Models (LLMs) involves identifying outputs, patterns, or behaviours that deviate significantly from what is...
What is Predictive Analytics? Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify patterns and forecast future outcomes. At its core, it...
What is Ordinary Least Squares (OLS)? Ordinary Least Squares (OLS) is a fundamental technique in statistics and econometrics used to estimate the parameters of a linear regression model. In simple...
In machine learning (ML), bias is not just a technical concern—it's a pressing ethical issue with profound implications. As AI systems become increasingly integrated into our daily lives, from...
What is Online Machine Learning? Online machine learning, also known as incremental or streaming learning, is a type of machine learning in which models are updated continuously as new data becomes...
What is Bagging, Boosting and Stacking? Bagging, boosting and stacking represent three distinct ensemble learning techniques used to enhance the performance of machine learning models. Bagging,...
What is a Bayesian Network? Bayesian network, also known as belief networks or Bayes nets, are probabilistic graphical models representing random variables and their conditional dependencies via a...
What is Knowledge Graph Reasoning? Knowledge Graph Reasoning refers to drawing logical inferences, making deductions, and uncovering implicit information within a knowledge graph. A knowledge graph...
What is Computational Linguistics? Computational linguistics is an interdisciplinary field that combines principles of linguistics and computer science to develop computational models and algorithms...
What is Knowledge Representation and Reasoning (KRR)? Knowledge Representation and Reasoning (KRR) are fundamental concepts in artificial intelligence (AI) that focus on how intelligent systems can...
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