A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.1
Ian Goodfellow, Yoshua Bengio & Aaron Courville
Machine learning is essentially a form of applied statistics with increased emphasis on the use of computers to statistically estimate complicated functions and a decreased emphasis on proving confidence intervals around these functions[.]2
Ian Witten, Eibe Frank & Mark Hall
[W]e are interested in improvements in performance, or at least he potential for performance, in new situations.
Things learn when they change their behavior in a way that makes them perform better in the future.
Learning implies thinking and purpose. Something that learns has to do so intentionally.
Experience shows that in many applications of machine learning to data mining, the explicit knowledge structures that are acquired, the structural descriptions, are at least as important as the ability to perform well on new examples. People frequently use data mining to gain knowledge, not just predictions.3
The result of running the machine learning algorithm can be expressed as a function y(x) which takes a new digit image x as input and that generates an output vector y, encoded in the same way as the target vectors. The precise form of the function y(x) is determined during the training phase, also known as the learning phase, on the basis of the training data. Once the model is trained it can then determine the identity of new digit images, which are said to comprise a test set. The ability to categorize correctly new examples that differ from those used for training is known as generalization. In practical applications, the variability of the input vectors will be such that the training data can comprise only a tiny fraction of all possible input vectors, and so generalization is a central goal in pattern recognition.4″
“Data science isn’t woven into our culture; it is our culture. We started with it at the heart of the business, rather than adding it to a traditional organizational structure, and built the company’s algorithms around our clients and their needs. We employ more than 80 data scientists, the majority of whom have PhDs in quantitative fields such as math, neuroscience, statistics, and astrophysics. Data science reports directly to me, and Stitch Fix wouldn’t exist without data science. It’s that simple.”
“Emotibot, a Chinese caring robot solutions provider, has secured USD 30 million in series B round funding, led by China Development Financial, followed by Cathay Financial Holdings. Its existing investors Ecovacs and Advantech Capital participated in this new funding round. Light House Capital served as exclusive financial advisor in the transaction.
Founded in 2015, Emotibot is an artificial intelligence company engaged in deploying deep learning, Chinese semantic understanding, emotional calculation and computer science to provide emotional robot solutions. The robot can read, see, listen, remember, self-learn, and understand a user’s emotions, and the affective states, emotions and intentions of the speaker.”
“One of the world’s largest banks explains how it uses natural language processing and intelligent assistants to improve customer service on the web and mobile. Read this insider glimpse into the future of banking and user interfaces.”