InfoQ Homepage Machine Learning Content on InfoQ
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Analyzing and Preventing Unconscious Bias in Machine Learning
This article is based on Rachel Thomas’s keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. Thomas talks about the pitfalls and risk the bias in machine learning brings to the decision-making process. She discusses three use cases of machine learning bias.
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Understanding Software System Behaviour with ML and Time Series Data
David Andrzejewski presented "Understanding Software System Behaviour with ML and Time Series Data". This article is a summary of his presentation and an overview on what to look out for. Know about the traditional approaches to time series, how to handle missing values, and know about possibly occurring seasonality in your data. Be careful about what threshold you set for anomaly detection.
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Can People Trust the Automated Decisions Made by Algorithms?
The use of automated decision making is increasing. These algorithms can produce results that are incomprehensible, or socially undesirable. How can we determine the safety of algorithms in devices if we cannot understand them? Public fears about the inability to foresee adverse consequences has impeded technologies such as nuclear energy and genetically modified crops.
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Get More Bytes for Your Buck
Lovethesales had to classify one million product data from 700 different disparate sources across a large domain. They decided to create a hierarchy of classifiers through utilizing machine learning, specifically Support Vector Machines. They learned that optimising the way in which the svms were connected together yielded vast improvements in the reuse of labeled training data.
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The Problem with AI
AI depends on "data janitorial" work, as opposed to science work, and there is a gulf between prototype and sandbox, and innovation and production.
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Machine Learning Techniques for Predictive Maintenance
In this article, the authors explore how we can build a machine learning model to do predictive maintenance of systems. They discuss a sample application using NASA engine failure dataset to predict the Remaining Useful Time (RUL) with regression models.
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Virtual Panel: Data Science, ML, DL, AI and the Enterprise Developer
InfoQ caught up with experts in the field to demystify the different topics surrounding AI, and how enterprise developers can leverage them today and thereby render their solutions more intelligently.
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Big Data Processing Using Apache Spark - Part 6: Graph Data Analytics with Spark GraphX
In this article, author Srini Penchikala discusses Apache Spark GraphX library used for graph data processing and analytics. The article includes sample code for graph algorithms like PageRank, Connected Components and Triangle Counting.
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Article Series: An Introduction to Machine Learning for Software Developers
Get an introduction to some powerful but generally applicable techniques in machine learning for software developers. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.
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Book Review: Andrew McAfee and Erik Brynjolfsson's "The Second Machine Age"
Andrew McAffee and Erik Brynjolfsson begin their book The Second Machine Age with a simple question: what innovation has had the greatest impact on human history?
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Real-World, Man-Machine Algorithms
In this article, we'll talk about the end-to-end flow of developing machine learning models: where you get training data, how you pick the ML algorithm, what you must address after your model is deployed, and so forth.
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Anomaly Detection for Time Series Data with Deep Learning
This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data. To make our discussion concrete, we’ll show how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM.