AI for Drug Discovery refers to the use of artificial intelligence techniques to help identify and develop new medicines. These systems can analyse large amounts of scientific data much faster than humans, finding patterns and connections that might otherwise be missed. By using AI, researchers can predict how different chemical compounds might affect the body,…
Computational neuroscience is the study of how the brain processes information using mathematical models, computer simulations, and theoretical analysis. It aims to understand how networks of neurons work together to produce thoughts, behaviours, and perceptions. Researchers use computers to simulate brain functions and predict how changes in brain structure or activity affect behaviour.
Sparse Gaussian Processes are a way to make a type of machine learning model called a Gaussian Process faster and more efficient, especially when dealing with large data sets. Normally, Gaussian Processes can be slow and require a lot of memory because they try to use all available data to make predictions. Sparse Gaussian Processes…
Kernel methods are a set of mathematical techniques used in machine learning to find patterns in data by comparing pairs of data points. They allow algorithms to work with data that is not easily separated or structured, by transforming it into a higher-dimensional space where patterns become more visible. This makes it possible to solve…
Gaussian Process Regression is a method in machine learning used to predict outcomes based on data. It models the relationship between inputs and outputs by considering all possible functions that fit the data, and then averaging them in a way that accounts for uncertainty. This approach can provide both predictions and a measure of how…
The Expectation-Maximisation (EM) Algorithm is a method used to find the most likely parameters for statistical models when some data is missing or hidden. It works by alternating between estimating missing data based on current guesses and then updating those guesses to better fit the observed data. This process repeats until the solution stabilises and…
Variational inference is a method used in statistics and machine learning to estimate complex probability distributions. Instead of calculating exact values, which can be too difficult or slow, it uses optimisation techniques to find an easier distribution that is close enough to the original. This helps to make predictions or understand data patterns when working…
Conditional Random Fields, or CRFs, are a type of statistical model used to predict patterns or sequences in data. They are especially useful when the data has some order, such as words in a sentence or steps in a process. CRFs consider the context around each item, helping to make more accurate predictions by taking…
Markov Random Fields are mathematical models used to describe systems where each part is related to its neighbours. They help capture the idea that the condition of one part depends mostly on the parts directly around it, rather than the whole system. These models are often used in situations where data is organised in grids…
Probabilistic Graphical Models are mathematical structures that use graphs to represent relationships between random variables. Each node in the graph stands for a variable, and the connections show how these variables influence each other. They help to break down complex systems into manageable parts, making it easier to understand and compute probabilities for different scenarios.