Machine learning in today’s world has two goals: one is to classify data using established models, and the other is to generate predictions about future outcomes using these models. To train a hypothetical algorithm for classifying data, it could employ computer vision of moles combined with supervised learning to detect malignant moles. Machine learning arose from the search for artificial intelligence as a scientific pursuit. Some academics were interested in making machines learn from data in the early days of AI as an academic topic. They tried different symbolic methods as well as what was then referred to as “neural networks,” which were largely perceptron’s and other models that were subsequently discovered to be reinventions of generalized linear models of statistics.
Machine learning, which was restructured as a separate field in the 1990s, began to develop. The goal of the field shifted from artificial intelligence to practical problems that could be solved. It switched its focus away from the symbolic approaches it had received from AI and toward statistics and probability theory methodologies and models. Many learning issues are phrased as the minimizing of a loss function on a set of data instances, which relates machine learning to optimization. The gap between the model’s predictions and the actual problem occurrences is expressed by loss functions. In terms of methodologies, machine learning and statistics are similar, but their main goals are different: statistics derives Inferences about the population based on a sample, whereas machine learning looks for generalizable predictive patterns.
Supervised learning algorithms create a mathematical representation of a set of data including both the inputs and the outputs that are sought. The information is referred to as training data, and it consists of a collection of training instances. There are one or more inputs in each training example a supervisory signal as the desired output. Each training sample is represented by an array or vector, sometimes referred to as The training data is represented by a matrix, and the feature vector is a feature vector. in the mathematical model.
Unsupervised learning methods take a collection of data with only inputs and detect structure in it, such as data point grouping or clustering. As a result, the algorithms learn from unlabeled, unclassified, and uncategorized test data. Unsupervised learning algorithms discover commonalities in the data and react depending on the existence or lack of each new piece of art, there are such commonalities in data, rather than responding to feedback.
Reinforcement learning is a branch of machine learning that studies how software agents should behave in a given environment in order to maximize some metric of cumulative reward.
The technique of lowering the amount of random variables that are taken into account by generating a set of primary variables is known as dimensionality reduction. In other words, it’s a method for lowering the size of the feature set, also known as the “number of features.” The majority of dimensionality reduction approaches fall into one of two categories: feature deletion or extraction.
In 1982, As a machine learning paradigm, self-learning was introduced. proposed, coupled with the crossbar adaptive array, a neural network capable of self-learning (CAA). It is a type of learning in which there are no external rewards or teacher recommendations. The self-learning algorithm CAA computes both actions and emotions (feelings) in consequence scenarios in a crossbar fashion.
Several learning algorithms are aimed at discovering more accurate representations of the training inputs. Principal component analysis and cluster analysis are two well-known examples. Feature learning algorithms, also known as representation learning algorithms, try to conserve the information in their input while also transforming it in a useful fashion, usually as a pre-processing step before doing classification or prediction.n

How emerging technologies and their fast application influence the world around us are evident from the fact that every business moot has crystal clear glimpses of it. So is in the case of Schneider Electric’s Innovation Summit Middle East & Africa held on May 18-19th in Dubai, UAE.
The theme of the summit was ‘Digital, the Path to the Sustainable Future’—a
very well attended moot that knew about a greener future and unlocking of new business opportunities.
Schneider transmitted a strong message saying: Together, we can redefine the boundaries of efficiencies by transforming the way you approach your business via digitization and redefining our partnerships through open ecosystems and software integration.
From Pakistan, a team of IMS Electric participated in the summit with positive and progressive inputs for the moot.
The participants expected to be able to future-proof their organizations through digitization; engaged with business leaders, subject matter experts, and change-makers to discover the future of energy management and automation for their business.
A team of leaders as featured speakers was in the summit. They included Luc Remont, Executive VP, International Operations, Schneider Electric, Walid Sheta,
Zone President MEA, Schneider Electric, Amel Chadli, VP, Digital Energy, New Energy Landscape and Energy Management Software for MEA, Schneider Electric, Mike Jamieson, Global Segment President – Consumer Packaged Goods, Schneider Electric, Stuart Clarke, Associate Director, Arup, Ross Smith, Digital Grid Regional Sales Director, EMEA, Schneider Electric and Michael Cacciari, Global Leader Business Value Consulting, AVEVA.
The business leaders heard the latest trends in digital innovation, technology shaping the future of energy management, and the path to a sustainable future.n

Saman-e-Shifa Foundation (SSF) and Mehran University of Engineering & Technology (MUET), Jamshoro have signed a Memorandum of Understanding aimed at Integrating and making a collaboration bridge/Link between Industry & Academia for an international standards (medical devices and equipment) Products globally acceptable by the medical Industry/commercial Market.
The product will be designed, developed, and manufactured through R&D, indigenization, TOT, and reverse engineering projects.
The products will be developed and manufactured by combined projects of university faculty, students, renowned independent researchers, and industry to have a strong sustainable industrial base in Pakistan for export of all types of biomedical devices and equipment. Prof. Dr. Tauha Hussain Ali, Vice Chancellor MUET joined as the chief guest of the event and was coordinated by Prof. Dr. Bhawani Shankar Chowdry Professor Emeritus / Advisor MUET, Jamshoro.
Prof Dr Syed Shahid Noor, President SSF signed the MOU on behalf of SSF.n

The Pakistan Engineering Council (PEC) has signed Memorandums of Understanding (MoUs) with several brands and companies to facilitate PEC-registered engineers to acquire services and facilities at concessionary rates.
In Lahore, the council’s PR Committee led by Engr Mukhtiar A. Shaikh signed four MoUs with various brands, companies, and health services. They include ChenOne, a known global brand, Engineers Accommodation Services located in Bhurban, Bukhari Travels, and NUST Hospital, Lahore.
A team of the committee including Engr. Abdul Qadir Shah, Engr. Abdul Rehman Shaikh, Engr. Mohsin Khan, Engr. Malik Malik, Nadeem Malik, Engr. Faisal Jawed, Engr. Khalil Shaikh and others visited Chen One head offices in Lahore where Mian Kashif, the CEO of the company welcomed the team. Both sides also looked into other possibilities of coordination keeping a view of a wide range of business activities of the group.
In this MoU, Chen One offered 20 percent concession on its products to all PEC engineers at over 40 outlets across Pakistan.
Besides ChenOne, engineers will also get 20 percent discount for Alley Group of Colleges, La Atrium and Sultan Basha (restaurants).
Another MoU was signed with Engr. Nadeem Malik, CEO United Engineering Society for concessionary accommodation at UES guest house in Bhurban. The company claims it is a quality accommodation where engineers will get a 30 percent discount. The committee members who visited Engr. Malik’s offices appreciated the offer.
The University of Lahore Hospital and Bukhari Travels were the next services that also offered concessions for engineers while acquiring their services. The former will give 21 percent and the latter will give 30 percent discount.
University of Lahore pro-rector Engr. Nasir Mehmood also promised to look into the possibility of giving a rebate of a certain percentage to the offspring of engineers in educational institutions of the university.
In Karachi, the committee signed an MoU with Essa Laboratory & Diagnostic Centre, a leading medical diagnostic service in Pakistan. The MoU was signed by Dr. Farha Essa Zaidi, Regional Head, East Zone, and Engr. Mukhtiar Shaikh, Vice Chairman Sindh. The Essa Laboratory will offer 10 to 30 percent concessions for diagnostic services to all engineers.
Moreover, Hashmanis Group of Hospitals has also given 20 to 40 percent discount on its services to engineers. An MoU was signed between Engr. Mukhtiar Shaikh and Mohammed Danish, Director Corporate of Hashmanis. — ER Report

A seminar on “Bridging the gap between Academia and Industry” was held at Haji Mehmood Senate Hall of Mehran University of Engineering & Technology.
The moot was attended by deans of faculties and academic heads of the university.
Waqas Qadeer Soomro, the founder, and CEO, SmartMentor.io was the guest speaker at the session.
During the event, an MoU between SmartMentor and MUET was signed for future collaborations on Academic Cooperation and empowerment of students with existing human capital, alumni, and Knowledge capital to create positive social impact.
Prof. Dr. Tauha Hussain Ali, Vice Chancellor MUET joined as the chief guest of the event coordinated by Prof. Dr. Bhawani Shankar Chowdry Professor Emeritus / Advisor MUET, Jamshoro.n