In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning. Besides teaching model evaluation procedures and metrics, we obviously teach the algorithms themselves, primarily for supervised learning. Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned. One of the skills that I want students to be able to take away from this course is the ability to intelligently choose between supervised learning algorithms when working a machine learning problem. Although there is some value in the "brute force" approach (try everything and see what works best), there is a lot more value in being able to understand the trade-offs you're making when choosing one algorithm over another.

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Instructors returning to high school and college classes this fall, take note: Grading your students' work is about to get a lot easier. A UC Berkeley professor and three former graduate students are putting the finishing touches on an artificial intelligence technology that groups answers and allows them to be graded en masse. The AI-boosted capability, now wrapping up beta testing before becoming available this fall, will be the newest feature of the online grading application Gradescope. The team launched the app as a company two years ago, in part to stem cheating. Having a digital record of a graded paper makes it hard to alter written answers and argue the paper was incorrectly graded.

I always seasoned my vocabulary with as many four-letter words as 50-cent ones, at least until my first child was born two years ago. That's when I found myself -- and I'm almost embarrassed to admit it -- watching my language. Something deep in my subconscious told me that profanity might harm him in some way, that even a fleeting expletive, like a curse word uttered while stumbling over a child gate, could do lasting damage. Because I was not only a new parent but also a cognitive scientist specializing in language, I decided to investigate the issue. And I'm happy to report that, nowadays, if I drop an f-bomb in front of my kid, I don't sweat it.

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When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously. "I can tell you very senior CEOs of major American car companies would shake my hand and turn away because I wasn't worth talking to," said Thrun, now the co-founder and CEO of online higher education startup Udacity, in an interview with Recode earlier this week. A little less than a decade later, dozens of self-driving startups have cropped up while automakers around the world clamor, wallet in hand, to secure their place in the fast-moving world of fully automated transportation. And these companies are hungry for talent and skill sets many don't have. "Uber has just bought a half-a-year-old company [Otto] with 70 employees for almost 700 million," Thrun said.

Matthew Ramirez was teaching writing classes to students at the University of California at Berkeley when he started to get frustrated. Mixing his experience as a teacher with some advanced learning technology, he and his business partner started WriteLab – a Berkeley, California-based software company that helps students strengthen their writing skills by providing quick, customized feedback. WriteLab can even adapt its feedback over time to students' individual writing styles. "Focus on problems that eliminate waste – wasted time, wasted energy, or wasted space," Ramirez advises.

Matthew Ramirez was teaching writing classes to students at the University of California at Berkeley when he started to get frustrated. He was spending the majority of his time giving repetitive feedback to students, and there wasn't enough time to provide truly constructive, in-depth feedback to each individual before the next essay was due. "Given the time constraints of a semester and the number of students in a class, it wasn't humanly possible to respond to everything I wanted to," Ramirez said in an interview. "Nor was it possible to work through multiple drafts with individual students." Ramirez took it upon himself to build a solution.

A machine-learning technique that has already given computers an eerie ability to recognize speech and categorize images is now creeping into industries ranging from computer security to stock trading. If the technique works in those areas, it could create new opportunities but also displace some workers. Deep learning, as the technique is known, involves applying layers of calculations to data, such as sound or images, to recognize key features and similarities. It offers a powerful way for machines to recognize similarities that would normally be abstruse to a computer: the same face seen from different angles, for instance, or a word spoken in different accents (see "10 Breakthrough Technologies 2013: Deep Learning"). The mathematical principles that underlie deep learning are relatively simple, but when combined with huge quantities of training data and computer systems capable of powerful parallel computations, the technique has resulted in dramatic progress in recent years, especially in voice and image recognition.

This introductory Machine Learning course taught by top Caltech professor Abu-Mostafa covers theory, algorithms and applications, with focus on real understanding.