ALGORITHMS, PART II
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Taught by Kevin Wayne and Robert Sedgewick
Brought to You by Princeton University and Coursera
Compiled by the Free Library in March 2014

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This course includes the video lectures (MP4) and English subtitles (SRT). This was one of my first Coursera batch-downloads, so please excuse the mess.

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Course information taken from http://coursera.org/course/algs4partII

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ABOUT THE COURSE:

Part II covers graph-processing algorithms, including minimum spanning tree and shortest paths algorithms, and string processing algorithms, including string sorts, tries, substring search, regular expressions, and data compression, and concludes with an overview placing the contents of the course in a larger context.

RECOMMENDED BACKGROUND:

You need basic familiarity with programming in Java and the algorithms and data structures from Algorithms, Part I. This course is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest (and some background) in programming.

SUGGESTED READINGS:

Although the lectures are designed to be self-contained, students wanting to expand their knowledge beyond what we can cover in this 7-week class can find much more extensive coverage of these topics in our ebook Algorithms, Part II (4th Edition), published by Addison-Wesley.

COURSE FORMAT:

There will be two lectures (75 minutes each) each week. The lectures are each broken into about 4-6 segments, separated by interactive quiz questions for you to to help you process and understand the material. In addition, there will be a problem set and a programming assignment each week and there will be a final exam.

FAQ:

-What algorithms and data structures are covered?

    Part I focuses on elementary data structures, sorting, and searching. Topics include union-find, binary search, stacks, queues, bags, insertion sort, selection sort, shellsort, quicksort, 3-way quicksort, mergesort, heapsort, binary heaps, binary search trees, red-black trees, separate chaining and linear probing hash tables, Graham scan, and kd-trees.

    Part II focuses on graph and string-processing algorithms. Topics include depth-first search, breadth-first search, topological sort, Kosaraju-Sharir, Kruskal, Prim, Dijkistra, Bellman-Ford, Ford-Fulkerson, LSD radix sort, MSD radix sort, 3-way radix quicksort, multiway tries, ternary search tries, Knuth-Morris-Pratt, Boyer-Moore, Rabin-Karp, regular expression matching, run-length coding, Huffman coding, LZW compression, and the Burrows-Wheeler transform.

-Are there any associated resources available on the web?

    Yes. Our free booksite contains synopses of the text, web exercises, Java implementations of all the algorithms covered (any many more) that are designed for you to be able to download and use with one click, test data, and many other resources.

-How does this course differ from Design and Analysis of Algorithms?

        The two courses are complementary. This one is essentially a programming course that concentrates on developing code; that one is essentially a math course that concentrates on understanding proofs. This course is about learning algorithms in the context of implementing and testing them in practical applications; that one is about learning algorithms in the context of developing mathematical models that help explain why they are efficient. In typical computer science curriculums, a course like this one is taken by first- and second-year students and a course like that one is taken by juniors and seniors.

-I'd like to take Algorithms, Part II, but missed Part I. What should I do?

    It depends on your background. If you have absolutely no familiarity with basic data types and classic sorting and searching algorithms, you probably need to wait for the next offering of Part I. If you have some familiarity with the basics, you may be able to get up to speed by studying our book and our booksite.

-I am/was not a computer science major. Is this course for me?

    Yes. This course is for anyone using a computer to address large problems (and therefore needing efficient algorithms). At Princeton, over 25% of all students take the course, including people majoring in engineering, biology, physics, chemistry, economics, and many other fields, not just computer science.

-If I have no familiarity with Java programming, can I still take the course?

    Our central thesis is that algorithms are best understood by implementing and testing them. Our use of Java is essentially expository, and we shy away from exotic features, so we expect you would be able to adapt our code to your favorite language. However, we require that you submit the programming assignments in Java. If you have some experience programming in another language, you might find it worthwhile to learn our programming model by studying our book An Introduction to Programming in Java: An Interdisciplinary Approach and associated free booksite.

-If I have not programmed before, can I still take the course?

    Probably not.

-Does Princeton University award credentials or reports regarding my work in this course?

    No certificates, statements of accomplishment, or other credentials will be awarded in connection with this course.