Algorithms drive most of the technology we see today: Google search and Google maps, Facebook, and Amazon.com all have algorithms tightly woven into their software. This class will be a quick introduction to algorithms. An algorithm is a procedure for accomplishing some task, usually with efficiency as a goal, and oftentimes implemented on a computer. We will lay the foundations for constructing and analyzing algorithms, using the examples of sorting a list of numbers and finding a number in a list.

Have you ever wondered how Google maps works? In this class we will go over the theory behind it, developing a fast way of finding the shortest path from A to B.

There are a million words in the English dictionary, but when you start typing one on your iPhone, you quickly get a list of autocompletes. How is that possible? In this class, we'll see how to store enormous datasets on a computer in ways that make lookups easy.

Is it okay if an algorithm works ‘almost all’ the time? In this class, we’ll see how computers can use randomness to run faster. We’ll give randomized algorithms for finding medians, for testing if a number is prime, and for finding structures in graphs. Along the way, we’ll prove that the probability that our algorithms fail is less than the probability that the computer spontaneously bursts into flames.

Three of you are splitting a pizza, but there are only two slices left. Who deserves the biggest piece? How can you make money in this situation? In this class, we'll explore the theory of auction design - a mix of computer science and game theory. Note: This class has not been blessed by Silvio Micali.

Algorithms drive most of the technology we see today: Google search and Google maps, Facebook, and Amazon.com all have algorithms tightly woven into their software. This class will be a quick introduction to algorithms. An algorithm is a procedure for accomplishing some task, usually with efficiency as a goal, and oftentimes implemented on a computer. We will lay the foundations for constructing and analyzing algorithms, using the examples of sorting a list of numbers and finding a number in a list.

Have you ever wondered how Google maps works? In this class we will go over the theory behind it, developing a fast way of finding the shortest path from A to B.

How can computers efficiently manage large amounts of data? In this class, we will answer this question. Learn how to quickly look up a name in a database and how to determine how much of your data lies in a given range. As a bonus, we will develop an efficient algorithm for string matching (a string is a sequence of characters or numbers).

Is it okay if an algorithm works 'almost all' the time? In this class, we'll see how computers can use randomness to run faster. We'll give randomized algorithms for finding medians, for testing if a number is prime, and for finding structures in graphs. Along the way, we'll prove that the probability that our algorithms fail is less than the probability that the computer spontaneously bursts into flames.

Flip a coin 100 times, and you've got an 86% probability of getting between 40 and 60 heads. Find out how you can use this fact to calculate the volume of any convex body, and why succeeding is more important than trying.

We'll look at the very large and very small in mathematics and computer science. Compared to these numbers, $10^70$ protons in the universe is NOTHING.

Biologists can identify what a particular sequence of DNA codes for using experimental methods, but this often takes a lot ...

Students in Lin Alg / QM can drop by and ask questions. There's no need to stay for the entire ...

Students in Lin Alg / QM can drop by and ask questions. There's no need to stay for the entire ...

Students in Lin Alg / QM can drop by and ask questions. There's no need to stay for the entire ...