Gartner says more than 3 million workers across the world will have a'robo boss' by 2018. High time businesses reorient skill development programs to help mid-level managers stay relevant. In July, the Vodafone-Idea merger was approved by the Competition Commission of India (CCI). The mega deal will make the shareholders of both companies become part of the largest telecom company in India, and reward them in the future. It will also create a situation that can quickly escalate into a nightmare.

A $2 million donation will launch new research at the Institute for Advanced Study (IAS) in Princeton to forge an understanding of how machine learning evolves. Machine learning -- sometimes called the leading edge of artificial intelligence -- is the rapidly developing computer technology behind self-driving cars, complex web searches, medical and science applications, and face and speech recognition. Machine-learning programs synthesize knowledge in a way that's analogous to how children learn. The programs take examples, generalize, and then develop rules and understanding about the world without being taught directly. With time, the programs become better at particular tasks.

This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a strongly adaptive regret bound that is a factor of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical results show that our algorithm outperforms state-of-the-art methods in learning with expert advice and metric learning scenarios.

Machine Learning and Deep Learning has brought the future here. Predicting the future has always been the most sought after skill in this world. How much money could you make if you could predict the price of a stock or if you could predict which color will be in fashion six months later? You can predict almost anything that you wish. The future will be in your own palms.

Information Technology is a very dynamic discipline which has over the years contributed so much to the way we live. Gone are the days when we used to solely rely on the noisy landlines for communication. Nowadays, we can send text, voice, audio and video data seamlessly using smart phones which most people never ever dreamt of. We are now in the Machine Learning era, which I believe presents lots of opportunities for Zimbabwe. Our society is quickly changing from an industry based society to an information based society.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

In my 25 years of working with large datasets, from developing early machine learning algorithms for multimedia systems in the 1990s to optimizing the email marketing infrastructure at GSI Commerce in the 2000s and now applying machine learning to big data to find actionable insights in real time, I've seen the convergence of machine learning and marketing firsthand. This year, I'm excited to see how machine learning (ML), an artificial intelligence (AI) discipline geared toward the technological development of human knowledge, has impacted the marketing big data ecosystem. I'm also intrigued by how much room I see for growth in the future. Machine learning techniques are being used to solve many diverse problems, and we stand to benefit as we move towards a world of hyper-converged data, channels, content, and context -- having the right conversation at the right time with the right person in the right way. For us marketers, ML is about finding nuggets of "predictive" knowledge in the waves of structured and unstructured data.

Every time a new tool or app is invented, a new word follows. So, let's tackle two that have been flying around our heads for the past few years: Machine Learning (ML) and Deep Learning (DL). Techies, business gurus, and marketers love these words and throw them around whether or not they understand the differences. Side Note: We know that this topic is old news, it's discussed continuously. Which is why we had to write about it, clearly it's not being fully understood because all the current content out there is either too simple or too complicated.

What is supervised machine learning and how does it relate to unsupervised machine learning? In this post you will discover supervised learning, unsupervised learning and semis-supervised learning. Supervised and Unsupervised Machine Learning Algorithms Photo by US Department of Education, some rights reserved. The majority of practical machine learning uses supervised learning. Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Teachers are often stretched thin. As classroom sizes get larger and resources dwindle, it can be a significant challenge for even the most qualified teacher to provide individual attention to every single child, especially those with special challenges or learning difficulties. As part of the National Science Foundation (NSF) Expeditions in Computing, researchers from Yale University are developing socially assistive robotics-- a new field of robotics that focuses on assisting users through social rather than physical interaction. A core part of their research is to design these robots to work with children, including those with challenges such as autism, hearing impairment, or those whose first language is one other than English. The goal is not to replace teachers, but to assist them, said Brian Scassellati, a professor of computer science, cognitive science, and mechanical engineering at Yale University and director of the NSF Expedition on Socially Assistive Robotics.