Ensemble learning is a technique in machine learning where multiple models, often called learners, are combined to solve a problem and improve performance. Instead of relying on a single model, the predictions from several models are merged to get a more accurate and reliable result. This approach helps to reduce errors and increase the robustness…
Gradient Boosting Machines are a type of machine learning model that combines many simple decision trees to create a more accurate and powerful prediction system. Each tree tries to correct the mistakes made by the previous ones, gradually improving the model’s performance. This method is widely used for tasks like predicting numbers or sorting items…
Neural Ordinary Differential Equations (Neural ODEs) are a type of machine learning model that use the mathematics of continuous change to process information. Instead of stacking discrete layers like typical neural networks, Neural ODEs treat the transformation of data as a smooth, continuous process described by differential equations. This allows them to model complex systems…
Knowledge tracing is a technique used to monitor and predict a learner’s understanding of specific topics or skills over time. It uses data from quizzes, homework, and other activities to estimate how much a student knows and how likely they are to answer future questions correctly. This helps teachers and learning systems personalise instruction to…
Domain adaptation is a technique in machine learning where a model trained on data from one environment or context is adjusted to work well in a different but related environment. This is useful when collecting labelled data for every new situation is difficult or expensive. Domain adaptation methods help models handle changes in data, such…
Active learning is a machine learning method where the model selects the most useful data points to learn from, instead of relying on a random sample of data. By choosing the examples it finds most confusing or uncertain, the model can improve its performance more efficiently. This approach reduces the amount of labelled data needed,…
Meta-learning is a method in machine learning where algorithms are designed to learn how to learn. Instead of focusing on solving a single task, meta-learning systems aim to improve their ability to adapt to new tasks quickly by using prior experience. This approach helps machines become more flexible, allowing them to handle new problems with…
Bayesian Neural Networks are a type of artificial neural network that use probability to handle uncertainty in their predictions. Instead of having fixed values for their weights, they represent these weights as probability distributions. This approach helps the model estimate not just an answer, but also how confident it is in that answer, which can…
Ghost Parameter Retention refers to the practice of keeping certain parameters or settings in a system or software, even though they are no longer in active use. These parameters may have been used by previous versions or features, but are retained to maintain compatibility or prevent errors. This approach helps ensure that updates or changes…
Subsymbolic feedback tuning is a process used in artificial intelligence and machine learning where systems adjust their internal parameters based on feedback, without relying on explicit symbols or rules. This approach is common in neural networks, where learning happens through changing connections between units rather than following step-by-step instructions. By tuning these connections in response…