For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in maths - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialisation aims to bridge that gap, getting you up to speed in the underlying maths, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data.

In what way will AI be incorporated into e-learning in the near future? E-learning has the potential to revolutionize education. For one thing, the internet and burgeoning AI technology have made e-learning more accessible than ever before. But e-learning also offers solutions to some of education's most pressing challenges, and in the future, it could serve to more adequately provide all students access to quality teaching. Everyone processes content in different ways and at different speeds.

Responding to questions over email and posted on forums, Jill had a casual, colloquial tone, and was able to offer nuanced and accurate responses within minutes. A robot has been teaching graduate students for 5 months and none of them realized. Here are just a few of artificial intelligence tools and technologies that will shape and define the educational experience of the future. Duolingo: voice recognition for language learning Duolingo is the world's most popular platform to learn a language. App predicts your word strength, figures out which sentences will help you best practice your weakest words/skills, recommends immersion practice documents (translations) based on your progress and estimates the quality of a translation-in-progress. Plexuss: college comparison and recruitment platform Plexuss facilitates contact between universities and future students, and aims to help students make an informed decision when it comes to choosing the right university.

Machine learning and AI are some of the biggest topics in the tech world right now, and Google is looking to make those fields more accessible to more people with its new Learn with Google AI website. Google has been pursuing AI education for a while, both with advanced projects like TensorFlow and more playful projects like cat doodles and a machine vision experiment meant to showcase AI projects in more practical ways. Google envisions the Learn with Google AI site serving as a repository for machine learning and AI, and it's meant to be a hub for anyone looking to "learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems." The site will apparently cater to all levels of AI enthusiasts, from researchers looking for advanced tutorials to beginners. The site also features a free course called Machine Learning Crash Course (MLCC).

One year back, I was a newbie to the world of Machine Learning. I used to get overwhelmed by small decisions, like choosing the language to code with, choosing the right online courses, or choosing the correct algorithms. So, I have planned to make it easier for folks to get into Machine Learning. I'll assume that many of us are starting from scratch on our Machine Learning journey. Let's find out how current professionals in the field reached their destination, and how we can emulate them on our journey. I will illustrate how you can learn Data Science by drawing a parallel between how Rafael Nadal learned to play tennis, and how you can learn Machine Learning.

Machine learning and AI are some of the biggest topics in the tech world right now, and Google is looking to make those fields more accessible to more people with its new Learn with Google AI website. Google has been pursuing AI education for a while, both with advanced projects like TensorFlow and more playful projects like cat doodles and a machine vision experiment meant to showcase AI projects in more practical ways. Google envisions the Learn with Google AI site serving as a repository for machine learning and AI, and it's meant to be a hub for anyone looking to "learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems." The site will apparently cater to all levels of AI enthusiasts, from researchers looking for advanced tutorials to beginners. The site also features a free course called Machine Learning Crash Course (MLCC).

Robots Can cost as low as $25,000* 250,000 purchased globally in 2016** *Source: Robots: The new low-cost worker, Dhara Ranasinghe, CNBC, April 10, 2015. The "average" US worker now spends 25% of their day reading or answering emails Fewer than 16% of companies have a program to "simplify work" or help employees deal with stress. The average mobile phone user checks their device 150 times a day. The "average" US worker works 47 hours and 49% work 50 hours or more per week, with 20% at 60 hours per week 40% of the US population believes it is impossible to succeed at work and have a balanced family life. FOMO We are all suffering from…….

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters.

A leading centre for AI education will open up to the world, as Imperial launches its first Massive Open Online Courses with Coursera. The move allows anyone with an internet connection to learn from some of the world's top researchers in artificial intelligence (AI), machine learning and mathematics. Professor Alice Gast, President of Imperial, said: "AI has the potential to transform many sectors. It is wonderful to have world-leading Imperial experts providing this opportunity to such a broad audience. Many will benefit from this exciting curriculum on the machine learning and mathematics underpinning the rapid advances in AI."

About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.