Mediaboss Marketing

Bonavita, M. et al. Machine learning for earth system observation and prediction. Bull. Am. Meteorol. Soc. 102, E710E716 (2021).

Article Google Scholar

Tuia, D. et al. Toward a collective agenda on AI for earth science data analysis. IEEE Geosci. Remote Sens. Mag. 9, 88104 (2021).

Article Google Scholar

Li, Y., Li, M., Li, C. & Liu, Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci. Rep. 10, 9952 (2020).

Article Google Scholar

Hedelt, P., Efremenko, D. S., Loyola, D. G., Spurr, R. & Clarisse, L. Sulfur dioxide layer height retrieval from Sentinel-5 Precursor/TROPOMI using FP_ILM. Atmos. Meas. Tech. 12, 55035517 (2019).

Article Google Scholar

Copernicus Services. https://www.copernicus.eu/en.

Daudt, R. C., Le Saux, B., Boulch, A. & Gousseau Y. Weakly supervised change detection using guided anisotropic diffusion. Mach. Learn. https://doi.org/10.1007/s10994-021-06008-4 (2021).

Castillo-Navarro, J., Le Saux, B., Boulch, A., Audebert, N. & Lefvre, S. Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study. Mach. Learn. https://doi.org/10.1007/s10994-020-05943-y (2021).

Sumbul, G., Ravanbakhsh, M. & Demir, B. BigEarthNet-MM: a large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]. IEEE Geosci. Remote Sens. Mag. 9, 174180 (2021).

Article Google Scholar

Roscher, R., Bohn, B., Duarte, M. F. & Garcke, J. Explainable machine learning for scientific insights and discoveries. IEEE Access 8, 4220042216 (2020).

Article Google Scholar

Kang, J. et al. Learning convolutional sparse coding on complex domain for interferometric phase restoration. IEEE Trans. Neural Netw. Learn. Syst. 32, 826840 (2021).

Article Google Scholar

Arcucci, R., Zhu, J., Hu, S. & Guo, Y. K. Deep data assimilation: integrating deep learning with data assimilation. Appl. Sci. 11, 1114 (2021).

Article Google Scholar

Buizza, C. et al. Data learning: integrating data assimilation and machine learning. J. Comput. Sci. 58, 101525 (2022).

Article Google Scholar

Mack, J., Arcucci, R., Molina-Solana, M. & Guo, Y. K. Attention-based convolutional autoencoders for 3d-variational data assimilation. Computer Methods Appl. Mech. Eng. 372, 113291 (2020).

Article Google Scholar

Brajard, J., Carrassi, A., Bocquet, M. & Bertino, L. Combining data assimilation and machine learning to infer unresolved scale parametrization. Phil. Trans. R. Soc. A. https://doi.org/10.1098/rsta.2020.0086 (2021).

Geer, A. J. Learning earth system models from observations: machine learning or data assimilation?. Phil. Trans. R. Soc. A. https://doi.org/10.1098/rsta.2020.0089 (2021).

Farchi, A., Laloyaux, P., Bonavita, M. & Bocquet, M. Using machine learning to correct model error in data assimilation and forecast applications. Quart. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.4116 (2021).

Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686707 (2019).

Article Google Scholar

Beucler, T. et al. Enforcing analytic constraints in neural networks emulating physical systems. Phys. Rev. Lett. 126, 098302 (2021).

Article Google Scholar

Nowack, P., Runge, J., Eyring, V. & Haigh, J. D. Causal networks for climate model evaluation and constrained projections. Nat Commun. https://doi.org/10.1038/s41467-020-15195-y (2020).

Watt-Meyer, O. et al. Correcting weather and climate models by machine learning nudged historical simulations. Geophys. Res. Lett. 48, e2021GL092555 (2021).

Article Google Scholar

Keisler, R. Forecasting Global Weather with Graph Neural Networks. Preprint at https://doi.org/10.48550/arXiv.2202.07575 (2022).

McGovern, A., Ebert-Uphoff, I., Gagne II, D. J. & Bostrom, A. The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences. Preprint at https://arxiv.org/abs/2112.08453 (2021).

Paul, S. & Ganju, S. Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning. Preprint at https://doi.org/10.48550/arXiv.2107.08369 (2021).

Schneider, R. et al. A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain. Remote Sens. 12, 3803 (2020).

Article Google Scholar

Stafoggia, M. et al. Estimation of daily PM10 and PM2.5 concentrations in Italy, 20132015, using a spatiotemporal land-use random-forest model. Environ. Int. 124, 170179 (2019).

Article Google Scholar

Schneider, R. D. S. Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data. Int. J. Appl. Earth Obs. Geoinf. 88, 110 (2020).

Google Scholar

Kloog, I., Nordio, F., Coull, B. A. & Schwartz, J. Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens. Environ. 150, 132139 (2014).

Article Google Scholar

Here is the original post:
ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction | npj Climate and...

These online automation courses can prepare you for a role as an RPA developer, tester, solution architect and more.

Learning about some of the core competencies involved in automative technologies will stand to you in your career. A good grounding in automation and machine learning is beneficial for developers, tech entrepreneurs and anyone with an interest in solving problems.

Some of the most in-demand jobs in the automation sector at the moment include RPA developers, solution architects, RPA controllers, testers and process mining consultants.

These roles require people who are willing to upskill and keep on top of the fast developments in the sector. Many businesses have embraced automation and machine learning to make their operations more efficient. Therefore, automation roles require a mix of technical skills and soft skills.

Doing a short course is a great way of ensuring your technical skills are up to industry standards. Whether youre a beginner or you have some experience, theres a course out there for you. Many on this list are free, and all are relatively inexpensive compared to college degrees.

Heres our pick of some of the best automation courses out there

Intelligent process automation (IPA) is a nascent aspect of the already widely used robotic process automation (RPA).

Both courses offer quick video tutorials that you can watch in your own time. Theyre run by Automation Anywhere and aimed at business users and developers.

The course provider recommends that you do the RPA course before the IPA course if you dont already have a good grounding in the former.

Despite its no frills title, this course actually offers a lot. It includes more than nine hours of on-demand video and 95 downloadable resources designed to help you in your quest to automate the boring stuff.

Aimed at office workers, administrators and academics who want to improve their productivity, its a good fit for beginners. It takes you through the process of downloading and installing Python.

Google offers a fast-paced practical introduction to machine learning. The 15-hour course features 25 lessons and around 30 exercises.

You can learn from Googles ML researchers using real-world examples and interactive visualisations of the algorithms at work.

Its recommended that you have some experience with programming and Python prior to doing the course.

This course is run by Google on Coursera as part of the tech giants Google Career Certificates training scheme. It is free to enrol.

At the end of the course, youll get a certificate which is shareable on LinkedIn. The programme can be completed in around six months if you put in around 10 hours a week as suggested. The course work can be completed in your own time and deadlines can be set based on your schedule.

Youll learn how to automate tasks by writing Python scripts, Use Git and GitHub for version control and solve IT problems.

Developed by lecturers from the University of Minnesota, this course is aimed at beginner to intermediate software developers.

It is free and takes around four months to complete. You will learn about black-box and white-box testing, automated testing, web and mobile testing, as well as formal testing theory and techniques.

By the end of the course, you will be able to plan and perform effective testing of your software.

For those looking for a longer course on automation, this Level 7 Springboard courses next intake is in January 2023.

Run by South East TU, it is Government subsidised for unemployed people. It lasts one year and delivery is a mix of online classes and in-person lectures on campus.

The course was developed in consultation with several automation and manufacturing companies in the south east region.

Learners will graduate with the skills to work in an in demand sector.

10 things you need to know direct to your inbox every weekday. Sign up for the Daily Brief, Silicon Republics digest of essential sci-tech news.

See the article here:
6 courses to help you get to grips with automation and machine learning - Siliconrepublic.com

By Rohit Malik

The housing market has developed dramatically in recent years, given changes in the ways people are buying, selling, and financing their homes. One of the primary reasons for this change is the revolutionary use of machine learning and artificial intelligence in the real estate market. While their contributions may not be evident, changes are apparent when comparing the present-day residential real estate market to that of the 20th century.

Thanks to machine learning, algorithms can now rapidly organize large quantities of data, sorting through property values, debt information, key home factors, and even consumer information. By simply providing their home preferences (such as number of beds, baths, amenities, and location), as well as their personal budget, homebuyers have the ability to create personalized options that save consumers time, effort, and money.

While personalization is definitely a plus, it is not the only benefit of AIs incorporation into the real estate market. While it is vital to know the value of a home before buying or selling, AI has revolutionized home value estimation. Given todays competitive real estate market, companies that can utilize AI to predict changes in rent and sales prices have a competitive advantage, as consumers rely on this data to buy and sell property.

As a consumer, buying or selling a home can be both complicated and overwhelming. By incorporating AI into the real estate market, however, it reduces the hassle, specifically in regards to communication. Because buying a home is a life-changing decision, each aspect of the home-buying process must be suitable for the buyer.

For any company, building a trust-based relationship with customers is of the highest concern. With the adoption of AI into the real estate market, it is evident that machine learning helps improve this relationship by making the home buying process as effortless as possible. Given the ever competitive real estate market, AI and machine learning continue to prove necessary to help more consumers effortlessly buy, sell, and finance their homes.

(Rohit Malik is the founder & CEO of online real estate marketplace, Clicbrics)

See the article here:
The role of machine learning and artificial intelligence in transforming residential real estate market - The Financial Express

Center director Murali Annavaram (second from right) and associate director Meisam Razaviyayn (far right) hosted the event (PHOTO CREDIT: USC Viterbi)

On May 23rd, the USC-Meta Center for Research and Education in AI and Machine Learning hosted its first major event. The center was established in Fall of 2021 with the goal of addressing the technological challenges related to making AI and machine learning sustainable, efficient, and scalable. Nearly 100 attendees including, Meta representatives and USC Viterbi students and faculty who work in the AI and Machine Learning spaces, attended the workshop and poster session.

Our center identified four pillars of support we can provide to the next generation of AI and Machine Learning researchers and industry leaders, said Murali Annavaram, Professor of Electrical and Computer Engineering and the centers inaugural director. Those pillars, research, teaching, fellowships, and outreach, were all addressed at this inaugural in-person event.

Research

Seventeen USC Viterbi students shared their research in AI and Machine Learning with visitors from Meta (PHOTO CREDIT: USC Viterbi)

The research focus of the event centered on a series of ML presentations on the efficiency, security, and privacy of machine learning algorithms, followed by an in-depth poster session. A total of 25 PhD students shared their research with the visiting Meta team. These events allowed Meta visitors to get a better understanding of the breadth and depth of work already being done at USC Viterbi in AI and Machine Learning. The face-to-face nature of the session allowed industry representatives to ask questions and collaborate with researchers on future ideas.

USC Viterbi already has a strong presence in AI and Machine Learning research, which is why we were selected to establish this center, said Meisam Razaviyayn, Assistant Professor of Industrial and Systems Engineering, and the centers Associate Director. The research sessions are a perfect opportunity to showcase our strength to our Meta partners and to establish future collaborations.

The event was a great opportunity to connect with folks in industry and exchange ideas. It was nice to present my work to people who might be able to apply our algorithms in practice to help protect peoples privacy, at Meta and beyond, said PhD student and presenter Andrew Lowy.

Teaching

As the field of AI and Machine Learning grows and technology continues to improve, teaching and courses that address this area can and must evolve alongside it. At this workshop, the USC-Meta center announced two important curriculum enhancements that will better prepare students for research and work in these fields. The electrical and computer engineering department is adding systems and implementation-oriented ML course offerings in the near future (pending university approvals) and the industrial and systems engineering department is significantly enhancing the curriculum of the Masters in Analytics program.

Fellowships

Center leaders announced the inaugural cohort of six new MS fellowship recipients, a first of its kind at USC. In fact, these fellowships represent the first comprehensive, all-tuition paid fellowships for MS students with diverse and rigorous academic backgrounds. The awards also will foster diversity and inclusion among MS students working in relevant areas of research.

The generous support of the USC Meta Center has provided an invaluable opportunity for Masters students to pursue their passion for AI, said Camillia Lee, USC Viterbi Associate Dean of Graduate Admission. The USC Meta fellowship will play a major role in achieving the Viterbi Schools goal of continuing to attract some of the most talented and diverse students in the country, and prepare them for future careers in AI and machine learning.

Outreach

During the centers board meeting, USC Viterbi and Meta discussed the importance of improving mentorship and support to students who are part of the center or supported by center fellowships. The team agreed that outreach in this area should be expanded beyond PhD students to also include MS, undergraduate students, and K-12 students.

In collaboration with USC Viterbis SURE (summer undergraduate research experience) program, the center will support multiple undergraduate student researchers visiting USC this summer. That program, which sees students from all over the country come to campus to be exposed to engineering research, will now enjoy more support from the USC-Meta center. Five high school students will also be supported and mentored by the center this summer as part of USC Viterbis SHINE program. Leaders of both those programs were quick to praise the centers support.

This type of support, mentorship, and encouragement is transformative for students who are underrepresented in STEM, said Katie Mills, co-director of the USC Viterbi K-12 STEM Center. SHINE alumni go on to leading research universities, majoring in STEM, with a strong sense of the benefits to society of research and technology. The USC Meta Center is making those opportunities more available at the local level.

The additional financial support for SURE from the USC-Meta center will allow us to bring even more students into this program and let us focus even more on the important and growing fields of AI and Machine learning, said Andy Jones-Liang, associate director of academic services.

Despite the already great progress made in the nascent 5 months since the inception of the center, the center directors have a vision to bring academic and industry researchers in ML to collaborate on compelling societal challenges and to provide mentoring support for the next generation of ML engineers.

Published on June 16th, 2022

Last updated on June 16th, 2022

Follow this link:
USC-Meta Center Brings Progress to AI Education and Research - USC Viterbi | School of Engineering - USC Viterbi School of Engineering

Datatonic, a leader for Data + AI consulting on Google Cloud, announced it has received the 2021 Google Cloud Specialization Partner of the Year award for Machine Learning.

Datatonic was recognized for the companys achievements in the Google Cloud ecosystem, helping joint customers scale their Machine Learning (ML) capabilities with Machine Learning Operations (MLOps) and achieve business impact with transformational ML solutions.

Datatonic has continuously invested in expanding their MLOps expertise, from defining what good MLOps looks like, to helping clients make their ML workloads faster, scalable, and more efficient. In just the past year, they have built high-performing MLOps platforms for global clients across the Telecommunications, Media, and e-Commerce sectors, enabling them to seamlessly leverage MLOps best practices across their teams.

Marketing Technology News:Pega Named a Leader in Real-Time Interaction Management by Independent Research Firm

Their recently open-sourced MLOps Turbo Templates, co-developed with Google Clouds Vertex AI Pipelines product team, showcase Datatonics experience implementing MLOps solutions, and Google Clouds technical excellence to help teams get started with MLOps even faster.

Were delighted with this recognition from our partners at Google Cloud. Its amazing to see our team go from strength to strength at the forefront of cutting-edge technology with Google Cloud and MLOps. Were proud to be driving continuous improvements to the tech stack in partnership with Google Cloud, and to drive impact and scalability with our customers, from increasing ROI in data and AI spending to unlocking new revenue streams. Louis Decuypere CEO, Datatonic

Marketing Technology News:MarTech Interview with Venkat Nagaswamy, CMO at Mitel

Google Cloud Specializations recognize partner excellence and proven customer success in a particular product area or industry, said Nina Harding, Global Chief, Partner Programs and Strategy, Google Cloud. Based on their certified, repeatable customer success and strong technical capabilities, were proud to recognize Datatonic as Specialization Partner of the Year for Machine Learning.

Datatonic is a data consultancy enabling companies to make better business decisions with the power of Modern Data Stack and MLOps. Its services empower clients to deepen their understanding of consumers, increase competitive advantages, and unlock operational efficiencies by building cloud-native data foundations and accelerating high-impact analytics and machine learning use cases.

Originally posted here:
Datatonic Wins Google Cloud Specialization Partner of the Year Award for Machine Learning - MarTech Series