Have you read headlines the past couple years on major fusion advances from private companies, or hear news about trillion dollar research labs in China or South Korea? Does it all seem a bit suspicious to you, or confusing? This class aims to make things more clear, with an MIT fusion scientist separating fact from fiction.

Ever laid awake at night wondering 'so how did they figure out Uranium had all that energy in it?" Ever pondered "hm, maybe the contents of my smoke detector would be a tasty snack?" Wonder no longer, as an MIT nuclear fusion PhD student explains to you all the ways rocks that are just lying around can shoot electrons, photons, and who knows what else straight through your prefrontal cortex.

Infinite clean energy? Nuclear hellfire? 30 years away forever? Come ask an MIT fusion scientist questions about the state of the field, as well as the future it will enable. If you don't have questions, come sit in on a nerd's ramblings about what she thinks about while working! It'll be fun :)

Nuclear fusion has long been a shared dream of humanity. From the discovery that it powers the sun in the 1920s, to the classified experiments across the world in the 1950s, to the massive public experiments of the '70s and '80s (nevermind the hoaxes!), it's always seemed just out of reach. In this class we give a background level of nuclear physics, discuss the history of the field up to now, and use that to examine the future of fusion. Spoilers: there's lots of reason to be excited!

Nuclear fusion has been touted as the energy of the future since before any of your parents were born, so why isn't it here yet? What is it? How does it work? Can we do it? Is it really clean? Could it get us to space? How about just power our homes? We'll answer some of these questions for you, through the example of MIT's awesome fusion program.

Fusion energy has been the great dream of the world for nearly a century, but it often feels like a pipe dream. No radioactive waste, 0 risk of meltdown, 100s of millions of years worth of fuel on Earth alone, high power density, it's on when you need it (no need for batteries). But why don't we have it? Or do we? This class will give you the background to understand how fusion works, what the current state of fusion research is, and where it's headed. Focus will be on MIT's initiative, and on giving you guys reasons to get hype! Spoilers: there are a lot of them :)

Have you ever tried putting math symbols in a word doc? Ever craved that crisp "I'm a real nerd" look in your work? Ever wanted to publish a research paper that'll win you worldwide acclaim? Well, I can't help with any of that, but I can show you how to start with LaTeX, a common typesetting language for getting pretty symbols like $$\pi$$ and $$A \nRightarrow B$$ into your papers.

Are you a math nerd kinda annoyed by the handwaviness of machine learning? Do you want a more rigorous understanding of how machines learn, what kinds of models exist and when they're valid? This class will focus on the mathematical background of statistical learning theory, and will hopefully provide a more balanced perspective on all the buzzword-y content going around that you might be interested in.

Ebola, influenza, tuberculosis, Zika - we hear about outbreaks of diseases like these all the time, but what actually are they? Where do these diseases come from, and how do they manage to infect so many people? Who are the scientists who control outbreaks, and how do they stop diseases from infecting more people? In this course, we will use the CDC's Morbidity and Mortality Weekly Report - a professional publication - to explore these questions and learn about the pathogens that make big headlines.

Come learn about how to work with formulas to make your spreadsheets as useful as possible!

Are you curious about what machine learning is? Or what you can do with it? Are you suspicious of how people treat it like magic? So am I! It's not magical, it's not anything super new, and using it without care can do a lot of harm. In this class, we'll talk about some common machine learning algorithms, the statistical assumptions behind them, common applications, and best practices.