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Category Archives: Machine Learning
Amazon’s Machine Learning University To Make Its Online Courses Available To The Public – Analytics India Magazine
In a recent development, Amazon announced that it will make online courses by its Machine Learning University available to the public. The classes were previously only available to Amazon employees.
The company believes that machine learning has the potential to transform businesses in all industries, but theres a major limitation: demand for individuals with ML expertise far outweighs supply. Thats a challenge for Amazon, and for companies big and small across the globe.
The Machine Learning University (MLU) was founded with an aim to meet this demand in 2016. It helped ML practitioners sharpen their skills and keep them abreast with the latest developments in the field. The classes are taught by Amazon ML experts.
The tech giant now plans to make these classes available to the ML community across the globe. It will include nine more in-depth courses before the year ends. As the blog post notes, by the beginning of 2021, all MLU classes will be available via on-demand video, along with associated coding materials. It will cover topics such as natural language processing, computer vision and tabular data while addressing various business problems.
By going public with the classes, we are contributing to the scientific community on the topic of machine learning, and making machine learning more democratic, said Brent Werness, AWS research scientist and MLUs academic director.
This initiative to bring our courseware online represents a step toward lowering barriers for software developers, students and other builders who want to get started with practical machine learning, he added.
Instead of a three-class sequence that takes upwards of 18 or 20 weeks to complete, in the accelerated classes we can engage students with machine learning right up front, shared Ben Starsky, MLU program manager.
The company said that similar to other open-source initiatives, MLUs courseware will evolve to improve over time based on input from the builder community. It also looking to rebuild its curriculum to further integrate dive into deep learning into class sessions.
The company wants to include as many important things as possible while offering flexibility in the way people can take these classes.
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.
PhD Research Fellowship in Machine Learning for Cognitive Power Management job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY – NTNU | 219138 -…
About the position
This is a researcher training position aimed at providing promising researcher recruits the opportunity of academic development in the form of a doctoral degree.
Bringing intelligence into Internet-of-Things systems is mostly constrained by the availability of energy. Devices need to be wireless and small in size to be economically feasible and need to replenish their energy buffers using energy harvesting. In addition, devices need to work autonomously, because it is unfeasible to operate them manually or change batteriesthere's simply too many of them. To make the best of the energy available, IoT devices should plan wisely how they spend their energy, that means, which tasks they should perform and when. This requires the development of policies. Due to the different situations, the various devices may find themselves in, it will also vary from device to device which policies are best, which suggests the use of machine learning for the autonomous, individual development of energy policies for IoT devices.
One special focus in this project is the modeling of the power supply of the IoT devices, that means, the submodule that combines energy harvesting and energy buffering. Both are processes that are highly stochastic and probabilistic and vary over time and with the age of the device yet have major impact on a devices ability to perform well. In addition, due to the constraints, the approach itself must be computationally feasible and not itself consume too much energy. Combining machine learning for power supplies with the application goals of the IoT device is therefore a research challenge.
You will reportto the Head of Department.
Duties of the position
Within this project, we will design and validate machine-learning approaches to model power supplies to know more about their current and future state, and energy budget policies that allow IoT devices to perform better and autonomously. The project is cross-disciplinary involving electronic design, software and statistical techniques and machine learning. Depending on the skills of the candidate, different aspect may be emphasized, for instance focusing on statistical modelling of relevant effects, transfer learning and model identification, and explainability of machine learning models. Experience with electronics may be beneficial but are not strictly required.
The research will be carried out in an interdisciplinary environment of several research groups, and under guidance of three supervisors,
The research environments include
Required selection criteria
The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is that you have completed a masters degree or second degree (equivalent to 120 credits) with a strong academic background in computer science, statistical machine learning, applied mathematics, communication- and information technology, electrical engineering, electronic engineering, or an equivalent education with a grade of B or better in terms ofNTNUs grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.
The appointment is to be made in accordance with the regulations in force concerningState Employees and Civil Servants and national guidelines for appointment as PhD, post doctor and research assistant.
Preferred selection criteria
In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability, in terms of the qualification requirements specified in the advertisement.
Salary and condition
PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 479 600 per annum, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.
The period of employment is 4 years including 25% of teaching assistance. Students at NTNU can also apply for this position as part of an integrated PhD program (https://www.ntnu.edu/iik/integrated-phd).
Appointment to a PhD position requires that you are admitted to the PhD programme in Information Security and Communication Technologywithin three months of employment, and that you participate in an organized PhD programme during the employment period.
The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.
It is a prerequisite you can be present at and accessible to the institution daily.
About the application
The application and supporting documentation to be used as the basis for the assessment must be in English.
Publications and other scientific work must follow the application. Please note that applications are only evaluated based on the information available on the application deadline. You should ensure that your application shows clearly how your skills and experience meet the criteria which are set out above.
The application must contain:
Joint works will be considered. If it is difficult to identify your contribution to joint works, you must attach a brief description of your participation.
NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.
Working at NTNU
A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.
The city of Trondheimis a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.
As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.
Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.
Questions about the position can be directed to Frank Alexander Kraemer, via firstname.lastname@example.org
Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway.
Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).
Pakistani applicants are required to provide information of Master Diploma from Higher Education Commission (HEC) https://hec.gov.pk/english/pages/home.aspx
Applicants with degrees from Cameroon, Canada, Ethiopia, Eritrea, Ghana, Nigeria, Philippines, Sudan, Uganda and USA have to send their education documents as paper copy directly from the university college/university, in addition to enclose a copy with the application.
Application deadline: 13.09.2020
NTNU - knowledge for a better world
The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.
Department of Information Security and Communication Technology
Research is vital to the security of our society. We teach and conduct research in cyber security, information security, communications networks and networked services. Our areas of expertise include biometrics, cyber defence, cryptography, digital forensics, security in e-health and welfare technology, intelligent transportation systems and malware. The Department of Information Security and Communication Technology is one of seven departments in theFaculty of Information Technology and Electrical Engineering
Deadline13th September 2020EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityTrondheimScopeFulltimeDurationTemporaryPlace of serviceNTNU Campus Glshaugen
Machine learning is pivotal to every line of business, every organisation must have an ML strategy – BusinessLine
Swami Sivasubramanian, Vice-President, Amazon Machine Learning, AWS (Amazon Web Services), who leads a global AI/ML team, has built more than 30 AWS services, authored around 40 referred scientific papers and been awarded over 200 patents. He was also one of the primary authors for a paper titled, Dynamo: Amazons Highly Available Key-value Store, along with AWS CTO and VP, Werner Vogels, which received the ACM Hall of Fame award. In a conversation with BusinessLine from Seattle, Swami said people always assume AI and ML are futuristic technologies, but the fact is AI and ML are already here and it is happening all around us.excerpts:
Bengaluru, August 12
The popular use cases for AI/ML are predominantly in logistics, customer experience and e-commerce. What AI/ML use cases are likely to emerge in the post-Covid-19 environment?
We dont have to wait for post-Covid-19, were seeing this right now. Artificial Intelligence (AI) and Machine Learning (ML) are playing a key role in better understanding and addressing the Covid-19 crisis. In the fight against Covid-19, organisations have been quick to apply their machine learning expertise in several areas, including, scaling customer communications, understanding how Covid-19 spreads, and speeding up research and treatment. Were seeing adoption of AI/ML across all industries, verticals and sizes of business. We expect this to not only continue, but accelerate in the future.
Of AWSs 175+ services portfolio, how many are AI/ML services?
We dont break out that number, but what I can tell you is AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure putting machine learning in the hands of every developer, data scientist and expert practitioner.
Then why has AWS not featured in Gartners Data Science and ML Platforms Magic Quadrant?
Gartner's inclusion criteria explicitly excluded providers who focus primarily on developers. However, the Cloud AI Developer Services Magic Quadrant does cite us as a leader. Also, the recently released Gartner Solution Scorecard, which evaluated our capabilities in the Data Science and Machine Learning space, scored Amazon SageMaker higher than offerings from the other major providers.
Where is India positioned on the AI/ML adoption curve compared to developed economies?
I think, India is in a really good place. I remember visiting some of our customers and start-ups in India, there is so much innovation happening in India. I happen to believe that transformation comes because at a ground level, developers start adopting technologies and this is one of those things where I think India, especially at a ground level when it comes to the start-up ecosystem, have been jumping into in a big way to adopt machine learning technology.
For example, machine learning is embedded in every aspect of what Freshworks, a B2B unicorn in India, is doing. In fact, they build something like 33,000 models and they are iterating and theyre trying to build ML models, again using some of our technologies like Amazon SageMaker. Theyve cut down from eight weeks to less than one week. redBus, which Im a big fan of as I travel back and forth between Chennai to Bengaluru, is also using some of our ML technologies and their productivity has increased. One of the key things we need to be cognizant of is that machine learning technology is not going to get mainstream adoption if people are just using it for extremely leading-edge use cases. It should be used in everyday use cases. I think even in India now, it is starting to get into mainstream use cases in a big and meaningful way. For instance, Dish TV uses AWS Elemental, our video processing service to process video content and then they feed it into Amazon Rekognition to flag inappropriate content. There are start-ups like CreditVidya, who are building an ML platform on AWS to analyze behavioural data of customers and make better recommendations.
The greater the adoption of AI/ML, the more job losses are likely as organisations fire people to induct skilled talent. Please comment.
One thing is for sure, there is change coming and technology is driving it. Im very optimistic about the future. I remember the days where there used to be manual switching of telephones, but then we moved to automated switching. Its not like those jobs went away. All these people re-educated themselves and they are actually doing more interesting, more challenging jobs. Lifelong education is going to be critical. In Amazon, my team, for instance, runs Machine Learning University. We train our own engineers and Amazon Associates on various opportunities and expose them to leading-edge technology such as machine learning. Now, we are actually making this available for free as part of the AWS Training and Certification programs. In November 2018 we made it free, and within the first 48 hours of us making this free, we had more than one lakh people registered to learn. So, there is a huge appetite for it. In 2012, we decided, every organisation within Amazon had to have a machine learning strategy, even when machine learning was not even actually considered cool. So Jeff and the leadership team said, machine learning is going to be such a pivotal thing for every line of business irrespective of whether they run cloud computing or supply chain or financial technology data, and we required every business group in their yearly planning, to include how they were going to leverage machine learning in their business. And no, we do not plan to was not considered an acceptable answer.
What AI/ML tools do AWS offer, and for whom?
The vast majority of ML being done in the cloud today is on AWS. With an extensive portfolio of services at all three layers of the technology stack, more customers reference using AWS for machine learning than any other provider. AWS released more than 250 machine learning features and capabilities in 2019, with tens of thousands of customers using the services, spurred by the broad adoption of Amazon SageMaker since AWS re:Invent 2017. Our customers include, American Heart Association, Cathay Pacific, Dow Jones, Expedia.com, Formula 1, GE Healthcare, UKs National Health Service, NASA JPL, Slack, Tinder, Twilio, United Nations, the World Bank, Ryanair, and Samsung, among others.
Our AI/ML services are meant for: Advanced developers and scientists who are comfortable building, tuning, training, deploying, and managing models themselves, AWS offers P2 and P3 instances at the bottom of the stack which provide up to six times better performance than any other GPU instances available in the cloud today together with AWSs deep learning AMI (Amazon Machine Image) that embeds all the major frameworks. And, unlike other providers who try to funnel everybody into using only one framework, AWS supports all the major frameworks because different frameworks are better for different types of workloads.
At the middle layer of the stack, organisations that want to use machine learning in an expansive way can leverage Amazon SageMaker, a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the ML process, empowering everyday developers and scientists to successfully use ML. SageMaker is a sea-level change for everyday developers being able to access and build machine learning models. Its kind of incredible, in just a few months, how many thousands of developers started building machine learning models on top of AWS with SageMaker.
At the top layer of the stack, AWS provides solutions, such as Amazon Rekognition for deep-learning-based video and image analysis, Amazon Polly for translating text to speech, Amazon Lex for building conversations, Amazon Transcribe for converting speech to text, Amazon Translate for translating text between languages, and Amazon Comprehend for understanding relationships and finding insights within text. Along with this broad range of services and devices, customers are working alongside Amazons expert data scientists in the Amazon Machine Learning Solutions Lab to implement real-life use cases. We have a pretty giant investment in all layers of the machine learning stack and we believe that most companies, over time, will use multiple layers of that stack and have applications that are infused with ML.
Why would customers opt for AWSs AI/ML services versus competitor offerings from Microsoft, Google?
At Amazon, we always approach everything we do by focusing on our customers. We have thousands of engineers at Amazon committed to ML and deep learning, and its a big part of our heritage. Within AWS, weve been focused on bringing that knowledge and capability to our customers by putting ML into the hands of every developer and data scientist. But we do take a different approach to ML than others may we know that the only constant within the history of ML is change. Thats why we will always provide a great solution for all the frameworks and choices that people want to make by providing all of the major solutions so that developers have the right tool for the right job. And our customers are responding! Today, the vast majority of ML and deep learning in the cloud is running on AWS, with meaningfully more customer references for machine learning than any other provider. In fact, 85 per cent of TensorFlow being run in the cloud, is run on AWS.
CORRECTING and REPLACING Anyscale Hosts Inaugural Ray Summit on Scalable Python and Scalable Machine Learning – Yahoo Finance
Creators of Ray Open Source Project Gather Industry Experts for Two-Day Event on Building Distributed Applications at Scale
Please replace the release with the following corrected version due to multiple revisions.
The updated release reads:
ANYSCALE HOSTS INAUGURAL RAY SUMMIT ON SCALABLE PYTHON AND SCALABLE MACHINE LEARNING
Creators of Ray Open Source Project Gather Industry Experts for Two-Day Event on Building Distributed Applications at Scale
Anyscale, the distributed programming platform company, is proud to announce Ray Summit, an industry conference dedicated to the use of the Ray open source framework for overcoming challenges in distributed computing at scale. The two-day virtual event is scheduled for Sept. 30 Oct. 1, 2020.
With the power of Ray, developers can build applications and easily scale them from a laptop to a cluster, eliminating the need for in-house distributed computing expertise. Ray Summit brings together a leading community of architects, machine learning engineers, researchers, and developers building the next generation of scalable, distributed, high-performance Python and machine learning applications. Experts from organizations including Google, Amazon, Microsoft, Morgan Stanley, and more will showcase Ray best practices, real-world case studies, and the latest research in AI and other scalable systems built on Ray.
"Ray Summit gives individuals and organizations the opportunity to share expertise and learn from the brightest minds in the industry about leveraging Ray to simplify distributed computing," said Robert Nishihara, Ray co-creator and Anyscale co-founder and CEO. "Its also the perfect opportunity to build on Rays established popularity in the open source community and celebrate achievements in innovation with Ray."
Anyscale will announce the v1.0 release of the Ray open source framework at the Summit and unveil new additions to a growing list of popular third-party machine learning libraries and frameworks on top of Ray.
The Summit will feature keynote presentations, general sessions, and tutorials suited to attendees with various experience and skill levels using Ray. Attendees will learn the basics of using Ray to scale Python applications and machine learning applications from machine learning visionaries and experts including:
"It is essential to provide our customers with an enterprise grade platform as they build out intelligent autonomous systems applications," said Mark Hammond, GM Autonomous Systems, Microsoft. "Microsoft Project Bonsai leverages Ray and Azure to provide transparent scaling for both reinforcement learning training and professional simulation workloads, so our customers can focus on the machine teaching needed to build their sophisticated, real world applications. Im happy we will be able to share more on this at the inaugural Anyscale Ray Summit."
To view the full event schedule, please visit: https://events.linuxfoundation.org/ray-summit/program/schedule/
For complimentary registration to Ray Summit, please visit: https://events.linuxfoundation.org/ray-summit/register/
Anyscale is the future of distributed computing. Founded by the creators of Ray, an open source project from the UC Berkeley RISELab, Anyscale enables developers of all skill levels to easily build applications that run at any scale, from a laptop to a data center. Anyscale empowers organizations to bring AI applications to production faster, reduce development costs, and eliminate the need for in-house expertise to build, deploy and manage these applications. Backed by Andreessen Horowitz, Anyscale is based in Berkeley, CA. http://www.anyscale.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20200812005122/en/
Media Contact:Allison Stokesfama PR for Anyscaleanyscale@famapr.com 617-986-5010
DPC – Google virtual workshop discusses the use of Machine Learning and AI technologies in the news industry – mediaoffice.ae
DPC - Google virtual workshop discusses the use of Machine Learning and AI technologies in the news industryLocal and regional journalists participate in two-day session The Dubai Press Club (DPC), in collaboration with Google News Initiative, held a virtual workshop on the use of Machine Learning and other Artificial Intelligence-powered technologies for journalists and the media.
The two-day session served as an introduction on how AI and Machine Learning (ML) can be leveraged to enhance the newsgathering process. More than 200 journalists based in the UAE and abroad tuned into the session, which was delivered under the Google News Initiative, a programme that strives to support quality journalism globally and train journalists on the latest Google tools.
Machine Learning and AI technologies have been deployed in almost every industry in the world. A wide range of organisations have adopted these technologies to handle redundant tasks and processes which help minimise costs and increase productivity. In the media industry, AI and Machine Learning have raised the efficiency of various tasks ranging from fact-checking to analysis of vast amounts of data.Maitha Buhumaid, Director of the Dubai Press Club said: We are pleased to work with a reputed global technology company like Google to train journalists on how they can benefit from Machine Learning and AI technologies in their everyday work. The session outlined the fundamentals of machine learning and how newsrooms around the world are using these technologies to enhance their operations.
Buhumaid added that the virtual workshop is part of a series of virtual events being organised by DPC to continue supporting regional media development even in the current environment.
Samya Ayish, Teaching Fellow, MENA at Google News Lab who led the virtual workshop, said New technologies such as artificial intelligence are increasingly playing a role in facilitating news gathering, production and distribution. The workshop, held in partnership with Dubai Press Club, aims to help journalists enhance their understanding of these technologies so that they can use them more effectively and to help facilitate their work."
The session focused on four modules, including how journalists can use ML, how a machine can learn, bias in machine learning and the future of ML-powered journalism. Case studies helped journalists understand how ML has been used in the media industry, how ML bias occurs and how it can be avoided. Trainees were also given a step-by-step overview of the ML training process.
Watch 3 Videos from Coursera’s New "Machine Learning for Everyone" – Machine Learning Times – machine learning & data science news – The…
Im pleased to announce that, after a successful run with a batch of beta test learners, Coursera has just launched my new three-course specialization, Machine Learning for Everyone. There is no cost to access this program of courses.
This end-to-end course series empowers you to launch machine learning. Accessible to business-level learners and yet pertinent for techies as well, it covers both the state-of-the-art techniques and the business-side best practices.
Click here to access the complete three-course series for free
After these three courses, you will be able to:
WATCH THE FIRST THREE VIDEOS HERE
MORE INFORMATION ABOUT THIS COURSE SERIES
Machine learning is booming. It reinvents industries and runs the world. According to Harvard Business Review, machine learning is the most important general-purpose technology of our era.
But while there are so many how-to courses for hands-on techies, there are practically none that also serve business leaders a striking omission, since success with machine learning relies on a very particular business leadership practice just as much as it relies on adept number crunching.
This specialization fills that gap. It empowers you to generate value with machine learning by ramping you up on both the technical side and the business side both the cutting edge modeling algorithms and the project management skills needed for successful deployment.
NO HANDS-ON AND NO HEAVY MATH.Rather than a hands-on training, this specialization serves both business leaders and burgeoning data scientists alike with expansive, holistic coverage of the state-of-the-art techniques and business-level best practices. There are no exercises involving coding or the use of machine learning software.
BUT TECHNICAL LEARNERS SHOULD TAKE ANOTHER LOOK.Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact.
IN-DEPTH YET ACCESSIBLE.Brought to you by industry leader Eric Siegel a winner of teaching awards when he was a professor at Columbia University this specialization stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.
Heres what you will learn:
DYNAMIC CONTENT.Across this range of topics, this specialization keeps things action-packed with case study examples, software demos, stories of poignant mistakes, and stimulating assessments.
VENDOR-NEUTRAL.This specialization includes several illuminating software demos of machine learning in action using SAS products, plus one hands-on exercise using Excel or Google Sheets. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.
WHO ITS FOR.This concentrated entry-level program is totally accessible to business-level learners and yet also vital to data scientists who want to secure their business relevance. Its for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether youll do so in the role of enterprise leader or quant. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants as well as data scientists.
LIKE A UNIVERSITY COURSE.These three courses are also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of this specialization is equivalent to one full-semester MBA or graduate-level course.
For more information and to enroll at no cost, click here
About the Author
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the long-runningPredictive Analytics Worldand theDeep Learning Worldconference series, which have served more than 17,000 attendees since 2009, the instructor of the end-to-end, business-oriented Coursera specializationMachine learning for Everyone, a popular speaker whos been commissioned formore than 100 keynote addresses, and executive editor ofThe Machine Learning Times. He authored the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sangeducational songsto his students. Eric also publishesop-eds on analytics and social justice. Follow him at@predictanalytic.