Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems): Ian H. Witten, Eibe Frank: 9780120884070: Amazon.com: Books

@machinelearnbot

This book is very easy to read and understand. Unlike Hastie's Statistical Learning book, it is not geared towards those with an expert level knowledge of statistics, and instead takes time to explain functions and formulas for the person with a decent but not extrordinary understanding of statistical/math concepts. For example, their description of a Gaussian was the clearest I've seen. On the other hand, if you're math/statistics background is considerable, you may find this book somewhat simplistic or tedious. The book has a good coverage of techniques and algorithms, although I was somewhat disappointed that they do not mention Influence Diagrams, considering the amount of coverage of both decision trees and Bayesian techniques.


Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (Computational Intelligence): Nikola K. Kasabov: 9780262112123: Amazon.com: Books

@machinelearnbot

The author has performed an excellent job in explaining the fundamental ideas and practical methods of different AI techniques. AI problems in the field ( pattern recognition, speech and image processing, classification, planning, optimization, control, time-series and analogy-based prediction, diagnosis, decision making and game simulations) are discussed and illustrated with examples . Especially useful are the comparisons between different techniques (AI rule Cbased methods, fuzzy methods, connectionist methods, and hybrid systems for knowledge engineering) used to solve the same or similar problems. The presented text is suitable for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering.The book¡ s appendices summarize data sets for the examples in the book. All data sets are available through anonymous FTP.


Google Brain Co-Founder Teams With Foxconn to Bring AI to Factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


Google Brain co-founder teams with Foxconn to bring AI to factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


Score more than 50 hours of training in machine learning for less than $40

Mashable

Computers may not wear tennis shoes (yet), but thanks to developing artificial intelligence technologies, they're smarter than ever before. Along with those technologies has come a relatively new category of computer science called machine learning, or ML. Similar to statistics, ML involves computer systems that utilize algorithms to automatically learn about data, recognize patterns, and make decisions, all without outside intervention or explicit directions from human beings. In the real world, you can find it being used in smart assistants like Siri and the Amazon Echo, in online fraud detection services, in the facial recognition feature that identifies photos of you on Facebook, and more recently, in Tesla's self-driving car. ML is distinctive in the world of AI in that it can be used to process vast amounts of data quickly, making it a desirable tech skill among job applicants not only in the fields of computer science and engineering, but also marketing, health care, finance, social media, and beyond.