In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.

Reinforcement learning (RL) is is the very basic and most intuitive form of trial and error learning, it is the way by which most of the living organisms with some form of thinking capabilities learn. Often referred to as learning by exploration, it is the way by which a new born human baby learns to take its first steps, that is by taking random actions initially and then slowly figuring out the actions which lead to the forward walking motion. Note, this post assumes a good understanding of the Reinforcement learning framework, please make yourself familiar with RL through week 5 and 6 of this awesome online course AI_Berkeley. Now the question that I kept asking myself is, what is the driving force for this kind of learning, what forces the agent to learn a particular behavior in the way it is doing it. Upon learning more about RL I came across the idea of rewards, basically the agent tries to choose its actions in such a way that the rewards that is gets from that particular behavior are maximized.

The first UK Robotics Week took place from 27 June to 1 July, with events and activities around the country showcasing leading UK technology and engineering research in robotics and autonomous design. Co-ordinated by the Engineering and Physical Sciences Research Council's UK Robotics and Autonomous Systems (UK-RAS) Network, and supported by the Royal Academy of Engineering, the Institution of Engineering and Technology and the Institution of Mechanical Engineers, the week sees robotic research groups from around the world visiting the UK to demonstrate their latest technologies in surgical robotics, field robotics, autonomous driving and unmanned aerial vehicles, while other challenges are set to engage and inspire school, college and university students. On 29 June, the Academy hosted a panel discussion on robotic ethics. The event also featured a Pepper and a NAO robot, from London Design and Engineering UTC, which will be some of the first in the UK to be used in an educational setting. The panel discussion can be found on Twitter at @RAEngNews.

This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy).

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you - this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his idea to add wheels, because when he pitched his idea people thought he was crazy. Surely no one would want to pull their own suitcase?

There is no question that artificial intelligence (AI) and automation are entering the workplace in many graduate level jobs, and this trend is likely to continue and quicken. Times Higher Education recently asked whether universities needed to rethink what they do and how they do it, given that artificial intelligence is beginning to take over many post-university careers. The implications and – most importantly – the potential benefits for education are significant, and perhaps not yet appreciated by higher education leaders. With that in mind, here are four examples of how AI can benefit universities. First, there is a new role for higher education, which is to equip graduates to work effectively alongside artificially intelligent systems.

Artificial Intelligence (AI) -- the technology that tries to mimic human behaviour and thought processes -- has decisively broken the size barrier. No more is it the preserve of big tech firms, as small and medium outfits and start-ups are increasingly using it to great effect. For instance, last month travel-search marketplace ixigo launched an AI-powered chatbot -- ixibaba -- for its users. The chatbot (an automated response system that gives users the feeling of chatting with another human) helps travellers find the cheapest travel deals, hotels, vacation destinations and things to do in a city. "Now AI is better positioned for an uptake, especially in domain-specific contexts, as a lot of Big Data is available. AI, one part of which is machine learning, is the next big thing," said Aloke Bajpai, co-founder and CEO at ixigo.

Education technology has the power to revolutionize education but with the integration of artificial intelligence, experts believed that it can be more beneficial, particularly in higher education and online learning. In an era where modern technology has become a valuable influence in the lives of humans, it's safe to assume that technology will be able to enhance the learning experience of educators and students, especially in higher education and online learning. As experts combined education technology (EdTech) and artificial intelligence (AI), a powerful tool to potentially transform education has been born. Due to the pervasiveness of technology today, the way students communicate and entertain themselves have changed. But some experts believed that the implementation of education technology alone in schools, colleges and universities across the nation is not enough to revolutionize education.

An anonymous reader writes: Jill Watson is an artificial intelligence bot, it is also Ashok Goel's teaching assistant. Ashok Goel, a computer science professor at Georgia Tech, hired Jill Watson to answer questions online for his students so that his teaching staff wasn't so overworked. On average, Goel and his staff receive more than 10,000 questions from students online each semester. So he decided to use IBM Watson, an artificial intelligence system designed to answer questions. After training and tweaking it for months, he was able to spit out good enough answers.