ML@B Blog

Machine Learning Crash Course: Part 1

Introduction, Regression/Classification, Cost Functions, and Gradient Descent

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation. Yet a typical explanation for machine learning sounds like this:

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”

Not very clear, is it? This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.

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Demo Day: September 2016

One of our main goals here at ML@B is to help students understand how to use machine learning in real-world situations. This semester, we’ve teamed up with Github, Grand Rounds, SAP, and Intuit to work on solving some of their problems through machine learning. In addition, we have members working on their own independent research projects with groups such as the International Computer Science Institute.

Just this Friday, we had our first demo day–a day where project members got to show what they were up to. Here’s a brief summary of what they had to show:

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Hello World!

One of the hottest and most exciting topics floating around these days is machine learning. People have created amazing things through machine learning, such as self-driving cars, mind-controlled prosthetics, and actual, readable dialogues in the style of Shakespeare. But just what is machine learning? Surely it’s something obscure and esoteric. Right? And surely the subject is so entangled in computer science and mathematics that it would make any reasonable student run the other way. Right?

That’s where Machine Learning at Berkeley comes in.

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