According to the National Center for Education Statistics, 42 percent of students who are bullied report that it happens in school hallways and stairwells. Thirty-four percent say they;re bullied in the classroom, right under their teachers; noses, yet the bullying problem continues in both public and private schools. In this episode, Mark shares three easy hacks to stop bullying at your school. Check out the show notes at http://hacklearning.org/bullying.

The 78-video playlist above comes from a course called Neural Networks for Machine Learning, taught by Geoffrey Hinton, a computer science professor at the University of Toronto. The videos were created for a larger course taught on Coursera, which gets re-offered on a fairly regularly basis. Neural Networks for Machine Learning will teach you about "artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc." The courses emphasizes " both the basic algorithms and the practical tricks needed to get them to work well." It's geared for an intermediate level learner – comfortable with calculus and with experience programming (Python).

Graham Johnson is an artist with a curious muse: the human cell. Twenty years ago he graduated from a quiet corner of Johns Hopkins where students draw cadavers instead of cutting them up. At first, Johnson stuck to the medical illustrator canon, animating cells in a classic, cartoonish style. But he dreamed of constructing three-dimensional, data-driven models that could capture all their beautiful complexity. For that, he'd need computers, lots of them.

Developing training programmes for a large group of sales or technical or services personnel is a challenging task as the programme is meant for a diverse group, and has to be engaging and meaningful for the participants. The programmes are mostly delivered at multiple locations, they have to be updated from time to time and at times, also require to be culturally sensitive to remain relevant as well as contemporary. Effective assessment strategy is also important to ensure the programmes meet the stated business objectives. In the digital era, there is a plethora of content available on the internet. A lot of it is free of cost via options such as MOOCs, Course Era, You Tube and others.

Neural Networks for Machine Learning will teach you about "artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc." The courses emphasizes " both the basic algorithms and the practical tricks needed to get them to work well." It's geared for an intermediate level learner – comfortable with calculus and with experience programming (Python).

We propose a generic approach to accelerate gradient-based optimization algorithms with quasi-Newton principles. The proposed scheme, called QuickeNing, can be applied to incremental first-order methods such as stochastic variance-reduced gradient (SVRG) or incremental surrogate optimization (MISO). It is also compatible with composite objectives, meaning that it has the ability to provide exactly sparse solutions when the objective involves a sparsity-inducing regularization. QuickeNing relies on limited-memory BFGS rules, making it appropriate for solving high-dimensional optimization problems. Besides, it enjoys a worst-case linear convergence rate for strongly convex problems. We present experimental results where QuickeNing gives significant improvements over competing methods for solving large-scale high-dimensional machine learning problems.

Professors at the University of California, Berkeley have fully immersed themselves and their students into the world of virtual and augmented reality. The group is currently working to develop ISAACS (Immersive Semi-Autonomous Area Command System), a software which will help humans discover a robot's intentions. "If there's a robot over there - scary or not - you look at this robot and have no idea what this robot is trying to do. You have no idea what this robot can do," said Dr. Allen Yang, executive director for the Center for Augmented Cognition at UC Berkeley in an interview. ISAACS will be accessible via AR headsets and has the capability of determining a robots intentions just by looking at it.

Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database search operators with an information theoretic ranking function called predictive relevance. Predictive relevance can be calculated by a fast sparse matrix algorithm based on posterior samples from CrossCat, a nonparametric Bayesian model for high-dimensional, heterogeneously-typed data tables. The result is a flexible search technique that applies to a broad class of information retrieval problems, which we integrate into BayesDB, a probabilistic programming platform for probabilistic data analysis. This paper demonstrates applications to databases of US colleges, global macroeconomic indicators of public health, and classic cars. We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.

It has been heavily criticised for its woeful response to removing jihadi videos and other shocking pages from the internet. But Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. The tech giant has long been condemned for its inability to control what is posted on its online platforms. Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. Google now wants the computers which monitor content being uploaded through YouTube and other channels to understand the nuances of what makes a video offensive.

It's now becoming common for me to hear that product owners/managers, technical managers and designers are turning to popular online courses to learn about machine learning (ML). I always encourage it -- in fact, I did one of those courses myself (and blogged about it). However, it's not always clear how much benefit someone whose goal is to design, support, manage, or plan for products that use machine learning will get from doing an online course in ML. These courses throw you into the deep end, asking you to start programming classifiers, when many non-technical team mates are only looking for sufficient knowledge to be able to work in teams that are creating an ML-driven product. It's a bit like wanting to drive a car, and'therefore' signing up to a course on combustion engines -- probably a little bit too detailed for practical day-to-day driving!