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Top 10 Machine Learning Algorithms For Beginners

#artificialintelligence

To give you an example of the impact of machine learning, Man group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. Initially developed in statistics to study the relationship between input and output numerical variables, it was adopted by the machine learning community to make predictions based on the linear regression equation. The mathematical representation of linear regression is a linear equation that combines a specific set of input data (x) to predict the output value (y) for that set of input values.


Top 10 Machine Learning Algorithms For Beginners

#artificialintelligence

To give you an example of the impact of machine learning, Man group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. Initially developed in statistics to study the relationship between input and output numerical variables, it was adopted by the machine learning community to make predictions based on the linear regression equation. The mathematical representation of linear regression is a linear equation that combines a specific set of input data (x) to predict the output value (y) for that set of input values.


Machine Learning Basics

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Before we start this article on machine learning basics, let us take an example to understand the impact of machine learning in the world. We can safely assume that machine learning has been a dominant force in today's world and has accelerated our progress in all fields. No matter which industry you look at, machine learning has dramatically altered it. Let's take an example from the world of trading. Man Group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. Machine learning has become a hot topic today, with professionals all over the world signing up for ML or AI courses for fear of being left behind. But exactly what is machine learning? It will be clear to you when you have reached the end of this article. Machine Learning, as the name suggests, provides machines with the ability to learn autonomously based on experiences, observations and analysing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow. Whereas in machine learning, we input a data set through which the machine will learn by identifying and analysing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset.


Cold War Analogies are Warping Tech Policy

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Or many Cold Wars, it seems. Pundits and politicians alike declaim that we're locked in a "new Cold War" with China, that we're in the throes of a "cyber arms race" with the rest of the world, and that Russia's election interference is, of course, today's 1960s contestation over political ideology. Justin Sherman (@jshermcyber) is a Cybersecurity Policy Fellow at New America. These tempting, easy-to-understand Cold War metaphors pervade policy discourse around emerging technologies like artificial intelligence and quantum computing. Peter Thiel notably deployed such metaphors in his recent (quite flawed) New York Times op-ed about AI and national security.


Artificial Intelligence: competitive advantage for financial institutions - Shield Financial Compliance

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The rise of financial technology has drawn the attention of regulators to FinTech firms and how they function. FinTech companies like any other regulated institutions are required to comply with a growing set of regulatory rules. One of the latest examples is the FCA's roll-out of Secure Customer Authentication (SCA) for e-commerce transactions. This will mean that card issuers, payments firms, and online retailers will have to follow more stringent authentication steps for European online payments over €30. However, the real regulatory pain for FinTech's is felt when scaling fast whilst entering new markets.


Using AI to Advance Science - DZone AI

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On a recent trip to Accenture's Dock facility in Dublin, they showcased an AI-based tool to recommend new recipes after having crunched through thousands of existing recipes to come up with potential new flavors. Though the work is interesting, perhaps such an approach is far more interesting when applied to innovation itself. A growing proportion of innovation today is what's known as recombinative, and sees existing ideas and concepts applied in fresh and creative ways. Researchers are increasingly deploying AI-based tools to help devise some of these possible roads to explore. For instance, a recent paper from researchers at Carnegie Mellon University and the Hebrew University of Jerusalem highlights an AI-driven approach to mine databases of patents and research papers for ideas that can be recombined into solutions for new problems.



A Tour of Machine Learning Algorithms

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In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and categorize the algorithms you may come across in the field. Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.


A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics

arXiv.org Machine Learning

In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction. However, the traditional prediction models are fixed while the conditions of the machines change over time, thus making the errors of predictions increase with the lapse of time. In this paper, we propose a general data renewal model to deal with it. Combined with the similarity function and the loss function, it estimates the time of updating the existing prediction model, then updates it according to the evaluation function iteratively and adaptively. We have applied the data renewal model to two prediction algorithms. The experiments demonstrate that the data renewal model can effectively identify the changes of data, update and optimize the prediction model so as to improve the accuracy of prediction.


A Neural Network for Semi-Supervised Learning on Manifolds

arXiv.org Machine Learning

Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation. Our algorithm uses channels that represent localities on the manifold such that correlations between channels represent manifold structure. The proposed neural network has two layers. The first layer learns to build a representation of low-dimensional manifolds in the input data as proposed recently in [8]. The second learns to classify data using both occasional supervision and similarity of the manifold representation of the data. The channel carrying label information for the second layer is assumed to be "silent" most of the time. Learning in both layers is Hebbian, making our network design biologically plausible. We experimentally demonstrate the effect of semi-supervised learning on non-trivial manifolds.