Open course ware

The goal of the course is basically to combine the materials that would be covered in two different courses in mechanical or chemical engineering, fluid dynamics and heat and mass transfer, to try and package that for materials engineers within one semester because the curriculum doesn't place enough emphasis on this material to spread it out over two. We try to teach all of the topics that are covered in both of the courses in mechanical or chemical engineering within the single semester. And that makes it, of course, a challenge to teach, a challenge to learn the sticking coefficient, the fraction that actually gets absorbed may not be quite as high as in a two semester sequence but that the goal is to teach all of that material. The students are evaluated on two different criteria which are the quizzes and exams of various types and the homework assignments. I weight the homework assignments very low in their overall grade, just enough to give them an incentive to ...

HODGES: The basic goal of 12.000 is to provide freshman at MIT with an opportunity to put together concepts and ideas that they get in a more formal way, in particular in the science core at MIT in their first year and put those in turn together with social issues, fundamental social issues in the context of big environmental problems. The subject changes from year to year in terms of its theme. We tend to focus on different areas at different times. But, on the other hand, the overarching goal is to have them think about how you approach really complicated problems that require multiple perspectives and don't necessary have any simple solutions. I mean most of the problems that they tend to deal with, in the other subjects that they take in the freshman year, have straightforward known solutions. So it's like taking a problem set, working on it and solving those problems in particular. But most of the problems that really keep you awake at night are not those kin...

In this chapter our goal is to introduce some metrics for measuring the performance of a circuit and then investigate ways to improve that performance. We'll start by putting aside circuits for a moment and look at an everyday example that will help us understand the proposed performance metrics. Laundry is a processing task we all have to face at some point! The input to our laundry "system" is some number of loads of dirty laundry and the output is the same loads, but washed, dried, and folded. There two system components: a washer that washes a load of laundry in 30 minutes, and a dryer that dries a load in 60 minutes. You may be used to laundry system components with different propagation delays, but let's go with these delays for our example. Our laundry follows a simple path through the system: each load is first washed in the washer and afterwards moved to the dryer for drying. There can, of course, be delays between the steps of loading the washe...

YUFEI ZHAO: The goal for the next few lecturers is to prove Freiman's theorem, which we discussed last time. And so we started with this tool that we proved called the Plunnecke-Ruzsa inequality, which tells us that if you have a set A now in an arbitrary abelian group, and if A has controlled doubling, it has bounded doubling, then all the further iterated sumsets have bounded growth, as well. So this is what we proved last time. And so we're going to be using the Plunnecke-Ruzsa inequality many times. But there are some other tools I need to tell you about. So the next tool is known as Ruzsa covering lemma. All right. So let me first give you the statement, and then I explain some intuition. I think the technique is more important than the statement. But in any case, here's what it says. If you have X and B-- they're subsets of some arbitrary abelian group-- if you have the inequality that X plus B is at most K times the size of B-- so the size of X plus...

The goal of baseball team is to make the playoffs. The A's approach was to get to the playoffs by using analytics. We'll first show how we can predict whether or not a team will make the playoffs by knowing how many games they won in the regular season. We will then use linear regression to predict how many games a team will win using the difference between runs scored and runs allowed, or opponent runs. We will then use linear regression again to predict the number of runs a team will score using batting statistics, and the number of runs a team will allow using fielding and pitching statistics. We'll start by figuring out how many games a team needs to win to make the playoffs, and then how many more runs a team needs to score than their opponent to win that many games. So our first question is how many games does a team need to win in the regular season to make it to the playoffs. In Moneyball, Paul DePodesta reduced the regular season to a math problem. He...