Summary: There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail. It's not as simple as it seems and here we try to take a more nuanced look. The result is not as threatening as many headlines make it seem. Is social bias in our models a threat to equal and fair treatment? There's even an entire conference dedicated to the topic: the conference on Fairness, Accountability, and Transparency (FAT* – it's their acronym, I didn't make this up) now in its fifth year.

The corporate training market is over $200 billion around the world[1] and it's going through a revolution. While we often think of training as programs or courses, a new paradigm has arrived, one I call "Learning in the Flow of Work." The corporate training industry has been around for decades and it has always been impacted by new technology. As the following chart shows, over the last 20 years we've been through four evolutions, each driven by technological and economic change. In the 1970s and 1980s, when I started my career, we learned in classrooms. The technology was slide projectors and "foils" (plastic laminated slides).

Databricks today unveiled MLflow, a new open source project that aims to provide some standardization to the complex processes that data scientists oversee during the course of building, testing, and deploying machine learning models. "Everybody who has done machine learning knows that the machine learning development lifecycle is very complex," Apache Spark creator and Databricks CTO Matei Zaharia said during his keynote address at Databricks' Spark and AI Summit in San Francisco. "There are a lot of issues that come up that you don't have in normal software development lifecycle." The vast volumes of data, together with the abundance of machine learning frameworks, the large scale of production systems, and the distributed nature of data science and engineering teams, combine to provide a huge number of variables to control in the machine learning DevOps lifecycle -- and that even before the tuning. "They have all these tuning parameters that you have to change and explore to get a good model," Zaharia said.

The constant need for smarter technology that learns and grows with you has become crucial, even when it comes to writing software code. This includes programming languages that understand and learn with you as you continue to write. Scala is one of the most impressive programming languages currently in the market. In order to deal with the shortcomings of Java language and restrictions that did not give the developer to do what he wanted, Scala was invented by Martin Odersky in 2001. According to Scala website, the programming language allows developers to have the best of both worlds – object oriented programming and functional programming.

In the field of data science, there are almost too many resources available: from Datacamp to Udacity to KDnuggets, there are thousands of places online to learn about data science. However, if you are someone who likes to jump in and learn by doing, Kaggle might be the single best location for expanding your skills through hands-on data science projects. While it originally was known as a place for machine learning competitions, Kaggle -- which bills itself as "Your Home for Data Science" -- now offers an array of data science resources. Although this series of articles will focus on a competition, it's worth pointing out the main aspects of Kaggle: Overall, Kaggle is a great place to learn, whether that's through the more traditional learning tracks or by competing in competitions. When I want to find out about the latest machine learning method, I could go read a book, or, I could go on Kaggle, find a competition, and see how people use it in practice.

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.

Successful workers are no longer defined by their ability to hone one specific set of skills and apply it throughout a linear, one-company career. Instead, the most successful workers are those with "liquid skills" – the ability to upskill, reskill and continuously hone existing capabilities. These workers accept that their current expertise could be outdated in the near future, and they will therefore continuously seek to acquire new, relevant skills. Certainly in a start-up culture we see the modern worker offering an organization a diverse range of skills, which are used to fill specific skills gaps. Within a single tenure at one particular organization, a worker can expect to bounce from role to role, meeting different skillset needs at different times, effectively having multiple careers without leaving the company.

The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during the middle school years. To tackle this challenge, we first estimate the expected knowledge state of students with respect to different mathematical skills using a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then combine the features corresponding to the DKT/DKT+ expected knowledge state with other features extracted directly from the student profile in the dataset to train several machine learning models for the STEM/non-STEM job prediction. Our experiments show that models trained with the combined features generally perform better than the models trained with the student profile alone. Detailed analysis of the student's knowledge state reveals that, when compared with non-STEM students, STEM students generally show a higher mastery level and a higher learning gain in mathematics.

Machine learning (ML) and artificial intelligence (AI) have become hot topics in many information processing areas, from chatbots to scientific data analysis. At the same time, there is uncertainty about the possibility of extending predominant ML technologies to become general solutions with continuous learning capabilities. Here, a simple, yet comprehensive, theoretical framework for intelligent systems is presented. A combination of Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is proposed as a generic approach for different types of problems. A prototype implementation is presented for document comparison using English Wikipedia corpus.