Our research in probabilistic machine learning spans algorithmic development, theoretical foundations, and applications in several domains.

At the heart of machine learning is the idea that machines can "learn" by detecting patterns in data. For instance, if you feed a machine learning algorithm thousands of images of cats and dogs ...

Overview of Machine Learning Libraries Numerous libraries such as TensorFlow, Scikit-Learn, and PyTorch offer robust functionalities for machine learning tasks.

Overview Beginner-friendly books simplify Python, R, statistics, and machine learning concepts.Practical examples and ...

For example, the user can control the summarisation algorithm by entering a text-based query. The query steers the method to produce a summary that accurately represents the needs of the user.