“Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.”— Pedro Domingos, amazon.com
“I'll bet the rest of my professional career that the future of your business is big data and machine learning applied to the business opportunities, customer challenges, and things before you.”— Eric Schmidt, businessinsider.com
“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.”— Tom Mitchell, amazon.com
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.”— Mark Cuban, youtube.com
“The distinction between deeper and wider models is; deeper models are better to capture more high-level abstractions that are important to differentiate particularly different classes (car vs horse) and wider models are better for fine-grain problems where the classes are close to each other and onl…”— Data Science Latin America, datasciencelatam.com
“In general, the more rules and facts we start out with, the more opportunities we have to induce new rules using ‘inverse deduction.’ And the more rules we induce, the more rules we can induce. It’s a virtuous circle of knowledge creation, limited only by overfitting risk and computational cost.”— Pedro Domingos, amazon.com
“If you try to come up with a set of rules that makes an exception, you’ll probably wind up with a worse answer than if you’d just ignored it. Learning a set of rules that gets [hyper-contextual instances] right is actually counterproductive: you’re better off ‘misclassifying’ it.”— Pedro Domingos, amazon.com
“Overfitting happens when you have too many hypotheses and not enough data to tell them apart.”— Pedro Domingos, amazon.com
“In ordinary life, bias is a pejorative word: preconceived notions are bad. But in machine learning, preconceived notions are indispensable.”— Pedro Domingos, amazon.com
“Learners can be divided into two major types: those whose representation has a fixed size, like linear classifiers, and those whose representation can grow with the data, like decision trees.”— Pedro Domingos, homes.cs.washington.edu
“As a rule of thumb, a dumb algorithm with lots and lots of data beats a clever one with modest amounts of it.”— Pedro Domingos, homes.cs.washington.edu
“Every learner must embody some knowledge or assumptions beyond the data it’s given in order to generalize beyond it.”— Pedro Domingos, homes.cs.washington.edu
“The output of different SVM classifiers can be combined simply by a weighted average of the estimates they produce. We combined in this way an SVM classifier which uses the set of color features F1 ∪ F2 ∪ F3 (with weight 1/3) and a second SVM classifier which uses the set of texture features G2 (wit…”— Philippe Golle, xenon.stanford.edu
“The success of our classifier does not come from the careful selection of a few colors with high predictive values, but rather from the combination of a large number of weakly predictive features.”— Philippe Golle, xenon.stanford.edu
“A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes.”— Noel Bambrick, kdnuggets.com
“With a 60% accurate classifier, the probability of solving a 12-image Asirra challenge is only about 0.2%.”— Philippe Golle, xenon.stanford.edu
“If you think that something might be a concern in the future, it is better to get historical data now.”— Martin Zinkevich, martin.zinkevich.org
“Self-driving cars will save lives — that’s not in question. Unfortunately, though, it’ll be responsible for a few along the way, and that’s an issue humans struggle with. While the car may have made the best decision based on the data available, to humans it’ll always be a machine that’s capable of…”— Brian Clark, thenextweb.com
“Even if an exact solution is far beyond reach, a reasonable approximate solution is quite feasible.”— Steven Abney, cs.columbia.edu
“Increasingly, we’re discovering that if we can learn things rather than writing code, we can scale these things much better.”— John Giannandrea, wired.com