Welcome. I'm Bob Sedgwick, professor of computer science at Princeton. This is our online course, Algorithms, developed by myself and Kevin Wayne here at Princeton. We're going to start with an overview, a discussion of why you might want to study algorithms, and then a little bit of discussion about the resources that you need to take this course. So what is this course? It's an intermediate level survey course on algorithms. We're going to concentrate on programming and problem solving in the context of real applications. And our focus is going to be on two things, algorithms which are methods for solving problems, and data structures which store the information associated with the problem and go hands and hands with algorithms. [COUGH] These are the basic topics that we'll cover in part one and part two of the course. The first part is data types sorting and searching. We'll consider a number of data structures and algorithms that are basic to all the methods we consider including stacks, queues, bags, and priority queues. Then, we'll consider classic algorithms for sorting, putting things in order. That's quick sort, merge sort, heat sort, and rated sorts. And we'll consider classic methods for searching, including binary search trees, red-black binary search trees, and hash tables. The second part of the course is for more advanced algorithms, including graph algorithms, classic graph searching algorithms, minimum spanning tree, and algorithms. Algorithms for processing strings including regular expressions and data compression. And then, some advanced algorithms that make use of the basic algorithms that we developed earlier in the course. So why should one study algorithms? Well, their impact is very broad and far reaching. From the Internet to biology, to commercial computing, to computer graphics, security, multimedia, cellphone networks and scientific applications. Algorithms are all around us. They're use for movies and video games, for particle collision simulation. They're used to study the genome and all manner of other applications. So that's one important reason to study algorithms, there impact is broad and far reach. Algorithms are also interesting to study because they have ancient roots. Now, the first algorithm we study goes back to 300 BC dating at least to Euclid. The concept of an algorithm was formalized, actually here at Princeton by Church and Turing in the 1930s, but most algorithms that we consider were discovered in recent decades. In fact, some were discovered by undergraduates in a course like this. In these plenty of other algorithms waiting to be discovered by students like you. The main reason that people study algorithms is to be able to solve problems that could not otherwise be address. For example, on the first lecture, we're going to talk about the network connectivity problem. Where the problem is given a large set of items that are connected together pair wise. Is there a way to get from one to another with a path through the connection. As you can see from this example, it's not clear whether or not there's such a path. We need the computer program to do it. In fact, we need an efficient algorithm to do it. In this case, the answer is that there is such a path. Another reason to study algorithms is for intellectual stimulation. Algorithms are very interesting object to study. Don Knuth, who wrote several books on algorithms and was a pioneer in the field, said that, an algorithm must be seen to be believed. You can't just think about an algorithm, you have to work with it. Another quote from Francis Sullivan says, the great algorithms are the poetry of computation. Just like verse, they can be terse, allusive, dense and even mysterious. But once unlocked they cast a brilliant new light on some aspect of computing. Algorithms are interesting for intellectual stimulation. Another reason many people study algorithms and I suspect many of you is it's necessary to understand good algorithms, efficient algorithms and data structures in order to be an efficient programmer. Linus Torvalds who created Linux says that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships. And I might add the algorithms to process them. Niklaus Wirth, another pioneer in computer science, wrote a famous book called Algorithms + Data Structures = Programs. [COUGH] Another reason, nowadays, to study algorithms, is that they have become a common language for understanding nature, algorithms are computational models and algorithmic models are replacing mathematical models and scientific inquiry. In the 20th century, scientist develop mathematical models to try to understand natural phenomenon, it soon became clear that those mathematical models were difficult to solve. It was difficult to create solutions to be able to test hypotheses against natural phenomena. So more and more and more, nowadays, people are developing computational models, where they attempt to simulate what might be happening in nature in order to try to better understand it. Algorithms play extremely important role this process and you'll see some of these in this course. Another important reason is that, if you known how to effectively use algorithms in data structures, you're going to have a much better chance at interviewing for a job in the technology industry than if you don't. Here's a bunch of reasons that I just went through for studying algorithms. Their impact's broad and far reaching. They have old roots and present new opportunities. That allows to solve problems that could not otherwise be addressed. You can use them for intellectual stimulation to become a proficient programmer. They might unlock the secrets of life in the universe and they're good for fun and profit. In fact, a programmer might ask, why study anything else? Well, there's plenty of good reasons to study other things, but I'll submit there's no good reason not to study algorithms. [COUGH] So for this course we have two resources that I want to talk about and make sure that people are familiar with before entering into the content. This is a publishing model that Kevin, Wayne, and I developed and have been using for many years. And we think it's a very effective way to support the kinds of lectures that we're going to be giving in this course. Down at the bottom ant it's optional for this course, we have a textbook, it's a traditional textbook that extensively covered the topic in the course. In fact, many more topics then we can present in lecture. And then, supporting that textbook is free, online material that we call the book site. You can go to the book site to see the lecture slides. But more important, there's code, there's exercises, there's a great deal of information there. In fact, maybe ten times, what's in the book, including a summary of the content. So during this course you'll be referring to the book site frequently while working online. People often ask about prerequisites. We're assuming that people who take this course know how to program. You know the basics of loops, arrays, functions. They have some exposure to object oriented programming and recursion. We use the Java language, but we don't dwell on details of Java. We mostly use it as an expository language. We do some Math but not advanced Math. If you want to review the material that we think is prerequisite to the material in this course, you can do a quick review by looking at sections 1.1 and 1.2 of the book, either at the book site or in the textbook. If you want an in depth review we have a full textbook called an introduction to programming in Java, an interdisciplinary approach. Others of booksite and a textbook as well. But the bottom line is you should be able to program and the quick exercise to get ready use to write a Java program on your computer perhaps using our programming model as describe on the bookside. We'll provide much more detailed information on that as we get into assignments. You can use your own programming environment if you're comfortable with one, or you can download ours. We have instructions on the web about how to do that.
