AI News, Crash Course in Machine Learning for Hackers

Crash Course in Machine Learning for Hackers

This interactive course will teach network security professionals machine learning techniques and applications for network data.

Participants will write code to prepare and explore their data and then apply machine learning methods for discovery. A non-exhaustive list of what will be covered include: Machine

Participants will write code to prepare and explore their data and then apply machine learning methods for discovery. A non-exhaustive list of what will be covered include:

Machine Learning

About this course: Machine learning is the science of getting computers to act without being explicitly programmed.

In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Stanford Engineering Everywhere

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines);

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Machine Learning For Medical Applications

Past decade has seen a quantum shift in how computers perform pattern recognition tasks.

A new paradigm, popularly described as machine learning, has been invented to “teach” computers how to solve problems such as classification, segmentation, and pattern recognition.

The old paradigm for solving these problems in classical computer vision consisted of explicitly programming into the software, task specific features.

For example, in order teach a program how to recognize human faces in an image, one would explicitly code into the software features that define a human face.

Machine learning concerns the recognition of complex patterns in observed data in order to automatically make decisions about patterns hidden in that data.

The success of face or other object recognition programs, ability to use images instead of keywords in a search query, and the fidelity with which modern translation programs are able to transform prose from one language to another, are just a few examples of machine learning algorithms.

A confluence of three trends —namely, massive computation power, easy availability of “big” data (e.g., through social media), and advent of a new computational architecture called deep neural networks— has made such rapid and widespread adoption of machine learning feasible.

Just as this paradigm has fundamentally altered the discourse on the social media and the internet, machine learning will fundamental alter nearly all aspects of medical practice of future.

This course will help a student acquire knowledge, skills and insight in machine learning in the domain of medical imaging and sensor data.

In the machine learning paradigm, the solution is found by providing the system with a large number of examples from which, in a training stage, the system tries to generalize.

API-driven services bring intelligence to any application

Developed by AWS and Microsoft, Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

More seasoned data scientists and researchers will value the ability to build prototypes quickly and utilize dynamic neural network graphs for entirely new model architectures, all without sacrificing training speed.

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