Machine Learning & BCL

Machine Learning & BCL

With the start of the new millennium and the accumulation of huge amounts of data, both science and commerce have transitioned into the 'big data era'. Nowadays, through cheap storage space, the internet and an advance in computing power, data is accessible and, in many cases, abundant. This is the case not only for large image databases or social media, but also for bioinformatic data, including biomedical images, genomics, protein structures and many more. The analysis of this data has numerous potential applications, for both industry and academia. To extract meaningful insights from 'big data', however, new algorithms need to be developed and existing ones be tailored to specific scientific questions. Machine learning algorithms build a model based on training samples in order to make accurate predictions for unseen data, and in this process uncover underlying data patterns. It is an umbrella term historically separated into Supervised Learning, Reinforcement Learning and Unsupervised Learning. Supervised Learning comprises methods where the training data is clearly labeled into classes, e.g. regression or classification algorithms. In contrast, Unsupervised Learning algorithms are designed to handle cases with no prior labeling of classes, like clustering or dimensionality reduction methods. Reinforcement learning has the goal to train a model through rewarding wanted behavior and therefore reinforcing certain action patterns. It is employed for tasks including autonomous driving cars, Artificial Intelligence (AI) for computer games and task optimization. Nowadays, Machine Learning is used in widespread in everyday applications, from smart phone navigation, to email spam filters, speech recognition or movie recommendations.