Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.

Robot Academy is an online platform that provides free-to-use undergraduate-level learning resources for robotics and robotic vision. The content was developed for two 6-week Massively Open Online Courses (MOOCs) that Corke taught in 2015 and 2016. This content is now available as individual lessons (over 200 videos, each less than 10 minutes long) or in masterclasses (collections of videos, around 1 hour in duration, previously a MOOC lecture). Unlike a MOOC, all lessons are available all the time. While the content is typically designed for undergraduate-level students, around 20% of the lessons require no more than general knowledge.

On 1st of August 2017, we will start a free online training program for Machine Learning, called Machine Learning Army Camp. The goal for me is to study and present 20 top books in Machine Learning, in 6 months, and to share my process with the community. We will integrate the knowledge from 20 books into a single big knowledge network, and you will get to see how it will grow over time. I show you here a network (click to see image) that I have built before for foundations in science in general, after having read around 80 books (philosophy of science, logic, math, computer science, machine learning, etc.). In ML Army Camp we will build one big network specifically for Machine Learning.

The future of work looks full of promise. Combining human brainpower with artificial intelligence, virtual reality and automatization will revolutionize how we work. "The future of work looks full of promise." Already, robotic enhancement is helping humans exceed their natural capabilities. AI is opening the door to real-time, personalized intelligent services, cutting waste and maximizing results.

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list. We decided to share this resource with you, and so here are Udemy's top selling courses.

Machine learning is the predictive heart of big data analytics, and one of the key skills that separates data scientists from mere analysts. But getting started with machine learning can be a challenge. Here are a few ways beginners can get off the ground with their machine learning adventure. Machine learning is a vast field with many different specialties, so it's quite easy for a beginner to get overwhelmed. For instance, one specialty called deep learning powers many of today's artificial intelligence breakthroughs.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.

Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years.

Mike Salem from Udacity's Robotics Nanodegree is hosting a series of interviews with professional roboticists as part of their free online material. Nick is a former robotics postdoc at Stanford and received his PhD in Control Systems from UC Berkeley. At Dash Robotics, Nick handles team-building and project management. You can find all the interviews here. We'll be posting one per week on Robohub.