Samples of Our Work: Graduate School #3
Every EssayMaster editing order includes a critique and a substantive edit. Please review the excerpt to understand the comprehensive nature of our editing. You may also review the full critique, edited essay, and original below.
Excerpt
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CRITIQUE
Dear [Fname],
Your original personal statement had some key strengths, including a fine, quantitative tone, experience in both academia and the industry, and specific rationale for choosing UCL. It’s clear you have completed the preparatory work towards your program at UCL, and none can doubt you numeric skills.
My job as editor was to improve the presentation of your strengths. To that end, I corrected all ESL errors, improved word choice for flow and comprehension, and sold your candidacy as an accomplished individual.
”My interest in technology is rooted deep in my childhood. “
Since this was the opening sentence, it was especially important to begin correctly. Idiomatically speaking, the correct wording is “rooted deep in my childhood” rather than “rooted profound from my youth.”
“At the end of the Artificial Intelligence course, I was involved in a project where I used a multi-threshold neuron model to solve a facial recognition problem. “
Here we had a key achievement. To that end, you model solved “a” rather than “the” facial recognition problem.
“immediately offer a reliable role in the workforce, contributing to making this world a better place.”
With a nice closing, we use “better place” as correct idiomatic English.
The resulting document has been streamlined, resulting in a fluent final presentation.
As part of my edit, I have also checked the “before” and “after” document in Grammarly, which provides sophisticated AI-assisted error-checking. While Grammarly is not perfect and does not scan for substance or organization as we do as human editors and while it can sometimes flag issues that are not actually issues, we have consistently found that successful essays tend to have scores above 90%. We note the original score on the document was 84% and the score on the revised draft is 98%.
Thank you for choosing EssayMaster. We wish you the very best with this logical, cogent personal statement and hope for your acceptance.
Sincerely,
EDITED ESSAY (the “After”)
My interest in technology is rooted deep in my childhood. Over the years, I have appreciated the beauty in logic and science, witnessing how it has shaped our world for the better. I am always fascinated by the famous question, “Can machines think?”, proposed by Alan Turing. I would like to know if intelligent agents can make decisions about what actions to take next to perform specific given tasks. These include the choice of where to turn and move in a maze, how hard to apply force to a robotic arm, or even the choice of which sensors to use and where to point them in order to solve a task. These are all considered problems of sequential decision making under uncertainty. With my high level of interest in the subject and foundational background experience through my Electrical and Electronic Engineering programme, I am committed to pursuing the Master of Science in Machine Learning programme at the University College London.
My undergraduate studies at the University of Nottingham have contributed to my interest in Machine Learning, and my Electrical and Electronic Engineer degree programme has provided a strong foundation for continuing studies. Through my Introduction to Computer Engineering course, I learned C Programming and software development strategies that I was able to apply in the design of a check-out system for Wal-Mart. In my Software Engineering Design course, I learned C++ programming and designed a bank account program for an ATM as part of our project. With an additional focus on object-oriented languages, I learned Java in the Web-Based Computing course, in which I designed and implemented a real-time, two-dimensional platform game by Netbeans. The degree programme, focusing on a strong mathematics background, provides me with the ability to work in challenging analytical and statistical environments. I was able to utilize my advanced mathematics background in my Artificial Intelligence course, the most relevant to machine learning during my undergraduate studies. In this course, I was introduced to the mathematical techniques behind the learning strategies of multilayer neural networks and was taught the learning strategies and architecture of radial basis function neural networks. Different fundamental learning strategies, such as supervised learning, unsupervised learning, and statistical learning theory, were compared.
At the end of the Artificial Intelligence course, I was involved in a project where I used a multi-threshold neuron model to solve a facial recognition problem. I had successfully designed a feedforward backpropagation network and radial basis network receptively by using MATLAB and a database of 400 images. By comparing the classification performance of the two methods, I found that radial curves proved to be more efficient than the backpropagation neural network. The complexity and dimensionality were reduced by robust principal component analysis and radial curve results, revealing a better performance useful in real-time face recognition systems. The system yields a 100% recognition rate accuracy for images from the database. After completing the facial recognition project, I also participated in the face optimization project to improve image resolution through deep learning. I used a large number of low-resolution images and inputted the high-resolution original photos of these images to train the algorithm so that it would determine the differences and increase the resolution skills from the comparison of the two. The facial recognition project, which merged artificial intelligence, machine learning, and deep learning methods, intensified my interest in the field and proved that machines could improve image resolution details accurately without using original photos, another impressive feat that results from the application of machine learning.
To complement my academic experience, my internship experience also prepares me for this degree programme. During my research internship in the Internet of Things and Computer Vision Department of Robert Bosch GmbH, I developed IoT-based real-time detection for the volume of square boxes inside a truck’s cabinet by using an ultrasonic sensor array. The designed system was an embedded device with integrated sensors, Arduino microcontroller, and GSM enable modules. The microcontroller was interconnected with a cloud server to collect, process, and analyse real-time sensor data and send it accurately to the IoT platform, Thingsboard. The generated real-time data can be stored both locally in the SD card and on the IoT platform cloud. The live volume graph was visualized locally by Python and by Dashboard on the IoT platform. This embedded system can provide a solution for monitoring the status of goods and further help achieve scientific management. This experience taught me how to quickly adapt to practical applications of machine learning and thoroughly consider the different solutions at my disposal, including the use of varying technologies and strategies.
UCL's programme attracts me for its rigorous course setting, close industry connections, and strong alumni networks. The programme consists of many optional modules and elective modules in various application fields, which provides me with foundational knowledge in machine learning and deep learning. The heavy emphasis on research and laboratory work, with the opportunity to gain real-world experience before programme completion, is also an important factor in offering a well-rounded programme. Additionally, the programme allows me to select career-orientated courses. I am particularly interested in the Advanced Deep Learning, and Reinforcement Learning and Multi-agent Artificial Intelligence modules, and I will go over module requirements in Machine Vision, Graphical Models, and Applied Machine Learning to determine a more personalized curriculum to fit my career goals. After graduation, I plan to join industry-leading companies such as Google, Intel, and Oracle to learn and grow alongside the developing AI industry. By using knowledge of machine learning and statistical methodologies learned from the program's courses, I can perform various data analytic tasks during scientific experiments in the technology sector and immediately offer a reliable role in the workforce, contributing to making this world a better place.
ORIGINAL ESSAY (the “Before”)
My advantage in innovation is established profound from my youth. Throughout the long term, I have valued the magnificence in rationale and science, seeing how it has molded our reality to improve things. I am constantly entranced by the popular inquiry, "Can machines think?", proposed by Alan Turing. I might want to know whether keen specialists settle on choices about what moves to make close to perform explicit given assignments. Like the decision of where to go and move in a labyrinth, or how difficult to apply power to an automated arm, or even the decision of which sensors to utilize and where to direct them all together toward settle an errand. These can are totally viewed as issues of consecutive dynamic under vulnerability. With my significant level of revenue in the subject and essential foundation experience through my Electrical and Electronic Engineering program, I am focused on seeking after the Master of Science in Machine Learning program at the University College London.
My undergrad learns at the University of Nottingham have added to my premium in Machine Learning, and my Electrical and Electronic Engineer certificate program has given a solid establishment to proceeding with examines. Through my Introduction to Computer Engineering course, I learned C Programming and programming improvement techniques that I had the option to apply in the plan of a registration framework for Wal-Mart. In my Software Engineering Design course, I learned C++ programming and planned a financial balance program for an ATM as a feature of our undertaking. With an extra spotlight on article arranged dialects, I learned Java in the Web-Based Computing course, in which I planned and executed a continuous, two-dimensional stage game by Netbeans. The degree program , zeroing in on a solid arithmetic foundation, furnishes me with the capacity to work in testing insightful and measurable conditions. I had the option to use my high level science foundation in my Artificial Intelligence course, the most pertinent to AI during my undergrad considers. In this course, I was presented the numerical methods behind the learning systems of multilayer neural organizations, just as shown the learning procedures and design of outspread premise work neural organizations. Distinctive crucial learning techniques, for example, managed learning, solo learning, and measurable learning hypothesis were analyzed.
Toward the finish of the Artificial Intelligence course, I was engaged with a task where I utilized a multi-limit neuron model to tackle the facial acknowledgment issue. I had effectively planned a feedforward backpropagation organization and outspread premise network openly by utilizing MATLAB and an information base of 400 pictures. By looking at the characterization execution of the two strategies, I discovered that outspread bends end up being more effective than the backpropagation neural organization. The multifaceted nature and dimensionality were decreased by hearty head segment examination and spiral bend results, uncovering a superior exhibition valuable progressively face acknowledgment frameworks. The framework yields 100% acknowledgment rate exactness for pictures from the information base. Subsequent to finishing the facial acknowledgment venture, I additionally partook in the face streamlining undertaking to improve picture goal through profound learning. I utilized an enormous number of low-goal pictures and information the high-goal unique photographs of these pictures to prepare the calculation with the goal that it would decide the distinctions and increment the goal aptitudes from the correlation of the two. The facial acknowledgment venture, which combined man-made brainpower, AI, and profound learning strategies, increased my premium in the field and demonstrated that machines can improve picture goal subtleties precisely without utilizing unique photographs, another great accomplishment that outcomes from the use of AI.
To supplement my scholastic experience, my entry level position experience additionally sets me up for this degree program. During my exploration entry level position in the Internet of Things and Computer Vision Department of Robert Bosch GmbH, I created IoT-based ongoing discovery for the volume of square boxes inside a truck's bureau by utilizing a ultrasonic sensor cluster. The planned framework was an inserted gadget with incorporated sensors, Arduino microcontroller and GSM empower modules. The microcontroller was interconnected with a cloud worker to gather, measure, and break down continuous sensor information and send it precisely to the IoT stage, Thingsboard. The produced continuous information can be put away both locally in the SD card and on the IoT stage cloud. The live volume diagram was envisioned locally by Python and by Dashboard on the IoT stage. This implanted framework can give an answer for checking the status of merchandise and is further useful in accomplishing logical administration. This experience showed me how to rapidly adjust in useful utilizations of AI, and altogether think about the various arrangements available to me, including the utilization of changing advancements and methodologies.
The program in UCL draws in me for its thorough course setting, close industry associations, and solid graduated class organizations. The program comprises of numerous discretionary modules and elective modules in an assortment of utilization fields, which gives me fundamental information in AI and profound learning. The substantial accentuation on examination and research facility work, with the occasion to pick up genuine experience before program finishing, is likewise a significant factor in contribution a balanced program. Furthermore, the program permits me to choose vocation orientated courses. I am especially inspired by the Advanced Deep Learning and Reinforcement Learning and Multi-specialist Artificial Intelligence modules and will go over module prerequisites in Machine Vision, Graphical Models, and Applied Machine Learning to decide a more customized educational plan to accommodate my profession objectives. After graduation, I intend to join industry-driving organizations, for example, Google, Intel, and Oracle to learn and develop close by the creating AI industry. By utilizing information on AI and factual systems gained from courses from the program, I can perform different information insightful assignments during logical trials in the innovation area and quickly offer a solid function in the labor force, adding to making this world a superior spot.