With the creation of Smart Cities high on the UAE government's agenda, a new course from the Center for Executive and Professional Education (CEPE) at American University of Sharjah (AUS) will help executives apply the benefits of Artificial Intelligence (AI) to their Smart City projects. The Certificate in AI for Smart Cities is being delivered by CEPE in conjunction with the AUS College of Engineering's Department of Computer Science and Engineering. Topics to be covered include Big Data, Machine Learning, Cyber-Physical Systems, Internet of Things, Cyber Security and Blockchain, and Cloud Computing. Participants require no prior knowledge of AI, as the course has been designed for mid- to high-level executives from across the Middle East tasked with developing Smart City solutions. It is an introductory course intended for those who want to better understand how AI can transform their operations, with a focus on learning from global best practice and case studies. AI holds enormous value for the UAE and wider GCC.

This course enables you to know more details about the data mining and ETL techniques. Whether you are new to Excel or an advanced user, Grey Campus Power BI course will cover what you need to know to become a Power BI User and how to gather and decode the data from multiple sources using advanced Ms Excel tools. Each and every topic mentioned above are explained in the Hands-on videos. Exercises and datasets included in this course are useful to practice and implement the concepts learned in this course. For your reference,additional material is also provided.

"The course is really very complete. In my case I did not know anything about the subject. However, the teacher explains so well that I was able to understand and complete all the topics. The teacher also answers the questions quickly and kindly. Machine Learning is one of the skills which is in high demand.

Machine Learning is currently one of the hottest topics out there. The working place of tomorrow is related to ML. No wonder that interest has drastically risen. The difficult question for beginners is how to get into it. From my personal experience the best way is to get one's hands dirty and apply machine learning in practice.

A machine learning learning PhD doesn't only open up some of the highest-paying jobs around, it sets you up to have an outsized positive impact on the world. This comprehensive guide on machine learning PhDs from 80,000 Hours (YC S15) will help you get started. The guide is based on discussion with six machine learning researchers including two at DeepMind, one at OpenAI, and one running a robotics start-up. Check out the highlights below. Machine learning involves giving software rules to learn from experience rather than directly programming the steps it takes.

In this article, we plan to walk readers through using our machine learning code to classify devices on a network. We have touched on this in previous blog posts about the Poseidon Software Defined Networking (SDN) project and how it relates to detecting lateral movement, as well as using machine learning (ML) to analyze network data. With that in mind, we've experimented with classifying devices using packet-capture data. We've made a few tools available to make it easier to try on your own network as well. The models that we'll be using run in combination with the Poseidon SDN project, and if you'd like to try that yourself, you can read about how to build your own Software-Defined Network with Raspberry Pis and a Zodiac FX switch or watch the video.

Artificial intelligence (AI), mixed reality, and cognitive science research sound like science fiction for today's classrooms, but this technology is available today. Innovation and technology are as integral to education today as chalkboards were in the past. And with the introduction of emerging, new, and proven technology-enhanced innovations, teachers are creating new ways of teaching and improving student learning, leading to a shift in pedagogy. Here are three of the latest innovations our district is using. Lumilo is a pair of mixed-reality smart glasses that provide teachers with continuous, real-time feedback about their students' learning, metacognition, and behavior, as well as potential effects of their own teaching.

Tech solutions are improving learning outcomes across the developed world, but Africa is being left out from the opportunity due to a lack of solutions delivered via basic mobile technology – a gap Kenyan edtech startup M-Shule is determined to address. The developed world is seeing advanced technologies such as artificial intelligence (AI) and machine learning have a great impact on improving the effectiveness of education, as these new solutions lend themselves to a more personalised learning process. However, the majority of edtech innovation is premised on reliable internet connectivity, and assumes users have access to smart devices. "This leaves out the mass majority of students in Sub-Saharan Africa," says Claire Mongeau, chief executive officer (CEO) of M-Shule. The idea for M-Shule was born in late 2016, when co-founders Mongeau and chief technology officer (CTO) Julie Otieno decided to create a mobile platform capable of bringing the benefits of AI-powered personalised learning to any and every student through SMS. "I worked in education for about six years in India, the US, and Kenya, where I realised that everywhere parents and students were investing so much time and energy into education, but there just weren't always the best tools available to them," Mongeau says.

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.

Amit Elazari Bar On is a doctoral law candidate (J.S.D.) at UC Berkeley School of Law and a CLTC (Center for Long-Term Cybersecurity) Grantee, Berkeley School of Information, as well as a member of AFOG, Algorithmic Fairness and Opacity Working Group at Berkeley. On 2017, Amit was a CTSP Fellow. We are told opaque algorithms and black-boxes are going to control our world, shaping every aspect of our life. They warn us that without accountability and transparency, and generally without better laws, humanity is doomed to a future of machine-generated bias and deception. From calls to open-the-black box to the limitations of explanations of inscrutable machine-learning models, the regulation of algorithms is one of the most pressing policy concerns in today's digital society.