Sir Martyn Lewis and I met back in April to discuss the impact of technology on humanity at The Club at The Ivy in London. It was a well-received debate, so we reconvened to tackle a new subject last month. As education is one of the key industries being disrupted by technology, and a subject both Martyn and I feel passionate about, it felt apt to put it on the agenda for the evening's discussion. The'Fourth Industrial Revolution' will see an increase in workforce automation. The Organisation for Economic Co-operation and Development (OECD) estimates that over the next 10 to 20 years, "14 percent of jobs are at high risk of being fully automated, while another 32 percent at risk of significant change".

Discovery Project: AI Super-Recruiter This project is about using a data science algorithm to emulate a professional recruiter in their job of finding good candidates for executive positions. A small team of students will work with a job recruiting firm in Asia. The firm will have one or more of their top recruiters mark resumes to explain what they look for in a set of 20 or more CVs for executive positions. By understanding what they look for, the team will develop a machine learning algorithm and/or rule based approach to selecting resumes. Results will be shown to the executive job search firm.

Start Date: Tuesday 21st April 2020 The updated certificate now includes 25 lecture weeks, our new Partnership with NAG Numerical NAG (Numerical Algorithms Group), additional practical lab sessions, an extended module 1 on Supervised Learning, new topic updates on Cloud Computing, Natural Language Processing, Practicalities of Neural Networks: CNN, Advanced Practicalities of Neural Networks: Generative NN, and a new full module on Times Series. Quantitative finance is moving into a new era. Traditional quant skills are no longer adequate to deal with the latest challenges in finance. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight. The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online.

Support Vector Machines (SVMs) can solve structured multi-output learning problems such as multi-label classification, multiclass classification and vector regression. SVM training is expensive especially for large and high dimensional datasets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The majority of the existing studies simply adopt the LRU (least recently used) replacement strategy for caching kernel values. However, as we analyze in this paper, the LRU strategy generally achieves high hit ratio near the final stage of the training, but does not work well in the whole training process. Therefore, we propose a new caching strategy called EFU (less frequently used) which replaces the less frequently used kernel values that enhances LFU (least frequently used). Our experimental results show that EFU often has 20\% higher hit ratio than LRU in the training with the Gaussian kernel. To further optimize the strategy, we propose a caching strategy called HCST (hybrid caching for the SVM training), which has a novel mechanism to automatically adapt the better caching strategy in the different stages of the training. We have integrated the caching strategy into ThunderSVM, a recent SVM library on many-core processors. Our experiments show that HCST adaptively achieves high hit ratios with little runtime overhead among different problems including multi-label classification, multiclass classification and regression problems. Compared with other existing caching strategies, HCST achieves 20\% more reduction in training time on average.

Graph Domain Adaptation with Localized Graph Signal Representations Yusuf Yi git Pilavcı, Eylem Tu g ce G uneyi, Cemil Cengiz and Elif Vural Abstract In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behaviour of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods. Keywords: Domain adaptation, spectral graph theory, graph signal processing, spectral graph wavelets, graph Laplacian 1 Introduction A common assumption in machine learning is that the training and the test data are sampled from the same distribution. Domain adaptation methods aim to provide solutions to machine learning problems by dealing with this distribution discrepancy. In domain adaptation, a source domain and a target domain are considered where the label information is mostly available for the data samples in the source domain, and few or none of the class labels are known in the target domain. The purpose is then to improve the learning performance in the target domain by making use Y. Y. Pilavcı is with the GIPSA Lab at Universit e Grenoble Alpes, Grenoble. C. Cengiz is with the Dept. of Computer Science and Engineering at Ko c University, Istanbul. Most part of this work was performed while the authors were at METU. 1 arXiv:1911.02883v1 A variety of approaches have been proposed so far for the domain adaptation problem. Some methods are based on reweighing the samples for removing the sample selection bias [1, 2]. Another common solution is to align the source and the target domains through feature space mappings.

I started my programming journey recently, having used computers only before for work, gaming, and the one high-school Maya animation class. I first looked at bootcamps and online degrees, but the cost of these programs, their length and commitment, and the fact that their curriculum is not always up-to-date, made me decide to go the self-taught way. I found a lot of media and tutorials offering to get me started, and trusting reviews for FreeCodeCamp.org, I completed their HTML, CSS, and Javascript components in about three weeks. I found it very intuitive in getting started.

The School of Science has announced that 14 of its faculty members have been appointed to named professorships. The faculty members selected for these positions receive additional support to pursue their research and develop their careers. Riccardo Comin is an assistant professor in the Department of Physics. He has been named a Class of 1947 Career Development Professor. This three-year professorship is granted in recognition of the recipient's outstanding work in both research and teaching.

The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R. Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language.

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The subject of artificial intelligence (AI) in education often centers around edtech breakthroughs and the ongoing evolution of the learning spaces inside schools. But AI is also experiencing growth in other sectors that have a direct impact on schools, presenting more areas for the education community to study. Take, for instance, construction and green energy resources. As schools look to cut costs, they are also increasing the adoption of sustainability programs. New state-of-the-art energy efficiency technologies may offer cost savings while reducing a school's carbon footprint.