One of the small drones aboard the ISS taught itself how to go around station with just one eye, and it was a lot harder than you might think. For starters, the SPHERE drone (that's short for Synchronized Position Hold Engage and Reorient Experimental Satellite) learned on its own by using machine learning. That method isn't typically used for space applications, because if it fails, it could result in a costly catastrophe. This is the first time a drone in space employed the technique to teach itself. Plus, the drone was operating in microgravity, floating around in a place where there's no up or down.

Multilevel models are a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries. Using regression techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques (like pooling or aggregating) prevents researchers from asking substantively interesting questions about how processes work at different levels. This two-day seminar provides an intensive introduction to multilevel models.

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

This team has decades of practical experience in working with Java and with billions of rows of data. If you are an analyst or a data scientist, you're used to having multiple systems for working with data. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

This specialised field demands multiple skills not easy to obtain through conventional curricula. So, be part of the data revolution by attending this workshop to learn the fundamentals of data science and machine learning and leave armed with practical skills to extract value from data. With the knowledge and skills gained from this workshop, you will be able to tackle complicated big data and machine learning challenges. Attend the workshop and develop the foundation level competence in data science and finding, manipulating, managing, interpreting and visualizing data.

I have studied Mathematics and got my degree and I am very interested in applying for a Master in Machine Learning. Of course my math background is high enough I believe. We did some coding like C, R, Matlab and worked SPSS. I've taken the Stamford Machine Learning coursera course and got a very very small taste of Machine Learning, so I guess what are other things I have to study to have a complete arsenal of tools. Thank you for your time.

This post is by Chirag Dhull, Product Marketing and Hang Zhang, Senior Data Science Manager, at Microsoft. Millions of people suffer from brain-related injuries and disorders every year. Being able to decode human perceptions from brain signals can benefit this population greatly. That's what inspired Stanford University neurosurgeon Dr. Kai Miller to team up with Microsoft to offer the inaugural Cortana Intelligence Competition: Decoding Brain Signals. "The brain is an electrical organ with over 100 trillion synapses, connecting more than 87 billion neurons," explains Dr. Miller.

Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field. We will be accepting contributed talks as well. The deadline for submission is Oct 24.

I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don't know how to get started… How do you get started in machine learning? The most common question I'm asked by developers on my newsletter is: I honestly cannot remember how many times I have answered it. In this post, I lay out all of my very best thinking on this topic. You are a developer and you're interested in getting into machine learning. You read some blog posts. You tried to go deeper but the books are dreadful.