algorithm


5 First Steps for Adopting Machine Learning in Finance Organizations

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For any executive who pursued a traditional path into the finance organization--business school, perhaps an accounting degree or an MBA in finance--the incessant media chatter about how machine learning will transform your business must be a little disconcerting. Most finance executives have little to no experience with the subject. And while many now have an idea of what machine learning is, few have a concrete understanding of how or where they can implement it. And yet, machine learning's influence on finance operations will inevitably grow, accelerating the long transition of the finance function from accounting steward and risk manager to valued business partner who helps drive strategy and identify and exploit business opportunities. A branch of artificial intelligence, machine learning refers to the use of algorithms, or computing models, that allow computers to become better at performing a given task through experience rather than programming.


Machine Learning Tutorial Machine Learning Basics Machine Learning Algorithms Simplilearn

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This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python. You can also go through the Slides here: https://goo.gl/aNmKbQ Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-... #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people's digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.


#MachineLearning Can Help Ensure #Cybersecurity - The Telecom TIMES

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The world has witnessed some serious data breach issues and the cybersecurity has been in question multiple times during the last few years. As per a report by Cisco, it's harder for the professionals to detect cyber threats due to the huge volume of malicious as well as legitimate encrypted web traffic. Therefore, machine learning is now being proposed to be used for cybersecurity. Some people think that machine learning is similar to artificial intelligence that is able to understand language and solve problems. Rather, machine learning is a part of artificial intelligence.


AI for cybersecurity: Friend or foe? - TechHQ

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Artificial intelligence (AI) has emerged as one of the key technologies in today's digital age, revolutionizing many industries, from customer service to autonomous vehicles. The technology is also becoming a key weapon in the fight against cybercriminals. The rise of data breaches has become a critical and concerning issue. In recent years we have seen a tsunami of cyber attacks around the globe, inflicting great costs to both businesses and customers. According to a report titled "Economic Impact of Cybercrime – No Slowing Down," by McAfee and the Center for Strategic and International Studies (CSIS), cybercrime cost the world between US$445 and US$608 billion in 2017.


Fast waveform detection for microseismic imaging using unsupervised machine learning Geophysical Journal International Oxford Academic

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Automatic arrival picking of certain seismic or microseismic phases has been studied for decades. However, automatic detection of continuous signal waveforms has been seldom addressed. In this paper, I propose a novel approach for automatically detecting the waveforms in the microseismic data. The waveform detection can be formulated into a classification-based machine learning (ML) problem, i.e., each data point in the microseismic record needs to be classified as either waveform or non-waveform. I use the classic K-means clustering based unsupervised machine learning algorithm to solve this problem.


Are you average? If not, algorithms might 'screw' you

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Are you average in every way, or do you sometimes stand out from the crowd? Your answer might have big implications for how you're treated by the algorithms that governments and corporations are deploying to make important decisions affecting your life. "What algorithms?" you might ask. The ones that decide whether you get hired or fired, whether you're targeted for debt recovery and what news you see, for starters. Automated decisions made using statistical processes "will screw [some] people by default, because that's how statistics works," said Dr Julia Powles, an Australian lawyer currently based at New York University's Information Law Institute.


CTRL T podcast: Artificial intelligence may become a human rights issue

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Welcome back to another glorious episode of CTRL T. This week, Henry Pickavet and I explore Amazon's new cashier-less stores that promise no waiting in line -- except to get in -- and Uber's newest C-level executive hire. Full disclosure, I went to USC but Noble was not a professor there at the time. Additional disclosure, I wish I could have had her as a teacher because she's smart as hell. Final disclosure, Henry applied to USC but was rejected. In her book, Noble discusses the ways in which algorithms are biased and perpetuate racism.


Building Products for the AI-Native Future – Hacker Noon

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The last decade was spent building products that were cloud native and mobile native, and this disrupted several industries and changed the way we live. Mobile made it possible for entrepreneurs to use camera and location to build products such as Instagram or Uber. As we look to the future, it is clear that something very exciting lies beyond cloud-native and mobile-native, and that is AI-native. This also means that entrepreneurs and product managers need to rethink both products and the way products are built to make them AI-native. AI-first products are closer to living things.


Is AI the new HR?

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Are your employees taking a flexible work policy too far? Anna O'Dea offers some tips for getting productivity back without taking away this desirable benefit The notion of AI often conjures up images of automation without human involvement. The modern conception of AI has its antecedents in world mythology, through figures such as Galatea or the Golem. Later medieval and enlightenment thinkers would expand on the concept, directly or indirectly contributing to the development of the modern computer in the process. The idea of AI has also flourished in science fiction, with one of the most famous examples found in Mary Shelley's Frankenstein.


Keeping artificial intelligence accountable to humans

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As a teenager in Nigeria, I tried to build an artificial intelligence system. I was inspired by the same dream that motivated the pioneers in the field: That we could create an intelligence of pure logic and objectivity that would free humanity from human error and human foibles. I was working with weak computer systems and intermittent electricity, and needless to say my AI project failed. Eighteen years later -- as an engineer researching artificial intelligence, privacy and machine-learning algorithms -- I'm seeing that so far, the premise that AI can free us from subjectivity or bias is also disappointing. We are creating intelligence in our own image.