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1 - 1 - Course Introduction (922)

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Welcome. I'm Bob Sedgewick, 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 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 in the prob, with the problem and go hands-in-hand with algorithms. These are the basic topics that we'll cover in part one and part two of the course. The first part is data type sorting and searching. We'll consider a number of data structures and algorithms that are basics 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 quicksort, mergesort, heapsort, and radix 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 shortest path 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 develop earlier in the course. So, why should one study algorithms? Well, Their input, impact is very broad and far reaching. From the internet to biology, to commercial computing, computer graphics, security, multimedia, social networks, and scientific applications, Algorithms are all around us. They're used for movies and video games, For particle collision simulation. They're used to study the genome in all manner of other applications. So, that's one important reason to study algorithms. Their impact is broad and far reaching. Algorithms are also interesting to study because they, they have ancient roots. First algorithm we studied 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 1930's. But most algorithms that we consider were discovered in recent decades. In fact, some were discovered by undergraduates in a course like this. And there's 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 it could not otherwise be addressed. For example, in 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 pairwise, Is there a way to get from one to another with a path through the connections? As you can see from this example, it's not clear whether or not there is such a path, We need a 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 objects 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, elusive, 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, good data structures in order to be a proficient 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 that process them. Niklaus Wirth, another pioneer in computer science, wrote a famous book called Algorithms + Data Structures = Programs. 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 twentieth century, Math, scientists 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 phenomenon. 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 an extremely important role in this process, And we'll see some examples of this in this course. Another important reason is that if you know effect, how to effectively use algorithms and 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. So, here's a bunch of reason that I just went through for studying algorithms. Their impact broad and far reaching, they have old routes and present new opportunities, they allow us to solve problems that could not otherwise be addressed. You could use them for intellectual stimulation to become an proficient programer, they might unlock the secrets of the life in the universe, and their good for fun and profit. In fact, a programer 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. 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, and it's optional for this course, we have a textbook. It's a traditional textbook that extensively covers the topics in the course, in fact, many more topics than we can present in lecture. And then, supporting that textbook is free online material that we call the Booksite. You can go to Book, the Booksite to see the lecture slides. But more important, there's codes, 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 Booksite 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 Booksite 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. There's a Booksite and a textbook as well. But the bottom line is, you should be able to program. And the quick exercise to get ready is to write a Java program on your computer, perhaps using our programming model as described on the Booksite. We'll provide much more detailed information on that as we get into the 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.

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Posted by: raipc on May 19, 2015

1 - 1 - Course Introduction (922)

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