Engineering


Machine Learning's Limits

#artificialintelligence

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. SE: Where are we with machine learning? What problems still have to be resolved? Aitken: We're in a state where things are changing so rapidly that it's really hard to keep up with where we are at any given instance. We've seen that machine learning has been able to take some of the things we used to think were very complicated and rendered them simple to do.


Decision Trees for Classification: A Machine Learning Algorithm Xoriant Blog

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Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split. An example of a decision tree can be explained using above binary tree.


Scaling Machine Learning to Recommend Driving Routes

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We built an app to predict potential earnings of a driver given his current location for next 8 hours in successive time intervals. The App also provided recommendations of next best pickup locations ranked based on driver preferences and behavior. Potential earning per recommended locations is predicted for several time interval such as next 15 minutes, 30 minutes, one hour, two hours and four hours. These options further helped in learning driver behaviour which is feedback to create more relevant recommendations. Our main aim was to maximize the revenue of the taxi services company by maximizing earnings per driver.


Feature Engineering for Machine Learning and Data Analytics

@machinelearnbot

Feature engineering plays a vital role in big data analytics. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.


Machine Learning in JavaScript with Propel – YLD Engineering Blog – Medium

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Propel is a new NumPy-like scientific computing and machine learning library for JavaScript, currently under heavy development on GitHub. It's main contributors are Ryan Dahl and Bert Belder (early contributors to the Node.js Machine learning can be very computationally intensive, involving manipulating large matrices of sometimes enormous amounts of data. Currently TensorFlow is a popular library for handling this, and is able to use GPUs to help process it's tasks and improve performance. TensorFlow's great and it's well-documented but let's be honest, it's a bit weird.


Understanding Feature Engineering: Deep Learning Methods for Text Data

#artificialintelligence

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. Working with unstructured text data is hard especially when you are trying to build an intelligent system which interprets and understands free flowing natural language just like humans. You need to be able to process and transform noisy, unstructured textual data into some structured, vectorized formats which can be understood by any machine learning algorithm. Principles from Natural Language Processing, Machine Learning or Deep Learning all of which fall under the broad umbrella of Artificial Intelligence are effective tools of the trade. Based on my previous posts, an important point to remember here is that any machine learning algorithm is based on principles of statistics, math and optimization.


Understanding Feature Engineering: Deep Learning Methods for Text Data

@machinelearnbot

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. Working with unstructured text data is hard especially when you are trying to build an intelligent system which interprets and understands free flowing natural language just like humans. You need to be able to process and transform noisy, unstructured textual data into some structured, vectorized formats which can be understood by any machine learning algorithm. Principles from Natural Language Processing, Machine Learning or Deep Learning all of which fall under the broad umbrella of Artificial Intelligence are effective tools of the trade. Based on my previous posts, an important point to remember here is that any machine learning algorithm is based on principles of statistics, math and optimization.


Influence of machine learning in Engineering education

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Recent news on Sophia the robot getting citizenship in the Saudi Arabia has widely attracted daily news and social media. Despite the debates and agitations on a robot getting recognition as humans, experts view this event as a phenomenal milestone in the research of AI.


Putting projects at the forefront

MIT News

Rose Wang loves to work on projects -- especially ones that exceed the bounds of her declared majors, economics and computer science. She thrives on do-it-yourself design solutions. Her latest involves making an aerodynamic drone. "We'll see how that goes," she says. So when Wang spotted a campus poster about a new project-centric program, the New Engineering Education Transformation (NEET), she went all in.


Online program wins engineering education award

MIT News

In collaboration with Boeing and edX, MIT has been honored with the 2017 Excellence in Engineering Education Collaboration Award by the American Society for Engineering Education (ASEE). The team was chosen for its design and development of a new four-course online professional certification program called Architecture and Systems Engineering: Models and Methods to Manage Complex Systems. The curriculum explores state-of-the-art practices in systems engineering and also demonstrates the value of models in enhancing system engineering functions and augmenting tasks with quantitative analysis. The program launched last September and ran through March. Nine faculty members from MIT and more than 25 industry experts from Boeing, NASA, IBM, Apple, General Electric, General Motors, and other companies developed content for the courses.