With representation learning,Deep learning must be major part of machine learning methods supported artificial neural networks . Deep learning models are supported artificial neural networks (ANN). Artificial Neural Networks (ANN) are computing systems. Artificial neuron are elementary units in a man-made Neural Network (ANN). Artificial neuron receives one or more inputs and sums them to supply an output.

This year, as Head of Science for the UCL Data Science Society, the society is presenting a series of 20 workshops covering topics such as introduction to Python, a Data Scientists toolkit and Machine learning methods, throughout the academic year. For each of these that I present and deliver I aim to create a series of small blogposts that will outline the main points with links to the full workshop for anyone who wishes to follow along. All of these can be found at out GitHub, and will be updated throughout the year with new workshops and challenges. The first workshop up then is the introduction to Python fundamentals, which acts as an introduction to the programming environment that members can use, along with covering the basics of Python such as Variables, Data types, and Operators. While some of the highlights will be shared here, the full workshop, including the problem sheet, can be found here.

In most online learning algorithms, the weights assigned to the misclassified examples (or support vectors) remain unchanged during the entire learning process. This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors. In this paper, we propose a new online learning method, termed Double Updating Online Learning", or "DUOL" for short. Instead of only assigning a fixed weight to the misclassified example received in current trial, the proposed online learning algorithm also tries to update the weight for one of the existing support vectors. We show that the mistake bound can be significantly improved by the proposed online learning method. Encouraging experimental results show that the proposed technique is in general considerably more effective than the state-of-the-art online learning algorithms."

We have started to carry out the first certification projects, whereby the applications are mainly found in the industrial environment but also in the consumer sector. In the next phases of the development cooperation, the current approaches will be expanded in order to be able to certify safety-critical applications, based on a broader spectrum of machine learning methods,

Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.",