Welcome to #ML Weekly!
Here is a roundup of Machine Learning content from this past week ordered by their social sharing volume. I gather the most shared Machine Learning content in Twitter, Facebook, Google+ and LinkedIn.
“How Machine Learning Makes Databases Ready for Big Data”
The promise of big data is incredibly enticing and, for most businesses, just as out of reach. The reason is simple: today’s databases are built upon 1970s math that was designed for 20th-century data requirements and hardware capabilities. This math has led to tree-structures and associated algorithms that are incapable of delivering the flexibility, scale and performance needed for the dynamic big data world.
“‘Machine teaching’ holds the power to illuminate human learning”
Human learning is a complex, sometimes mysterious process. Most of us have had experiences where we have struggled to learn something new, but also times when we’ve picked something up nearly effortlessly.
“How Machine Learning Changes the Game”
Machine learning will improve compliance, cost structures and competitiveness — but insurers must overcome cultural obstacles.
“New software that use geometric matching and machine learning to mimic humans’ perception – DeepStuff.org”
Kalogerakis and his doctoral student Zhaoliang Lun in the College of Information and Computer Sciences at UMass Amherst, with Alla Sheffer from the University of British Columbia, presented their new algorithm at one of the world’s largest computer graphics conferences, the annual Association for Computing Machinery’s (ACM) Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 2015, going on this week in Los Angeles.
“Managing unbalanced data for building machine learning models”
What is a two-class data science problem? How to handle unbalanced 2-class problems?
“Top-10 machine-learning and data-mining algorithms”
Machine learning deals with hundreds of algorithms that have various modifications. When selecting an appropriate class of algorithms and an algorithm within the class, you should closely consider your problem, define what you should measure or predict and which tools you are going to use for this purpose.
“Automatic classification of object code using machine learning”
Recent research has repeatedly shown that machine learning techniques can be applied to either whole files or file fragments to classify them for analysis. We build upon these techniques to show that for samples of un-labeled compiled computer object code, one can apply the same type of analysis to classify important aspects of the code, such as its target architecture and endianess. We show that using simple byte-value histograms we retain enough information about the opcodes within a sample to classify the target architecture with high accuracy, and then discuss heuristic-based features that exploit information within the operands to determine endianess. We introduce a dataset with over 16000 code samples from 20 architectures and experimentally show that by using our features, classifiers can achieve very high accuracy with relatively small sample sizes.
“Training: Introduction to Machine Learning and Data Mining”
Machine learning automatically recognizes complex, previously unknown, novel, and useful patterns and information in all types of data. Data driven algorithms are the wave of the future and their results improve as the amount of data increases. Machine learning algorithms are used in search engines, image analysis, multimedia database retrieval, bioinformatics, industrial automation, speech recognition, and many other fields. This survey course covers the concepts and principles of a large variety of data mining methods, equips you with a working knowledge of these techniques and prepares you to apply them to real problems.
“Machine Learning Guru wanted at Report Bee”
Report Bee is a new age data analytics company that mixes Maths, Art and Statistics to deliver beautiful interactive insights that are transforming the education ecosystem
“Commercialising Product Ready Machine Learning”
Chatterbox Labs, a machine learning and data science company, provide a machine learning framework for short form text data