CS2013-final-report

What is the format of the course
The material is presented in two 1.5 hour lectures per week with weekly quizzes and a programming assignment,
supported by a one-hour section where details pertinent to assignments and exams are covered by TAs teaching
smaller groups of students. An alternative format is to use online lectures supplemented by two 1.5 hour sections,
one devoted to discussion of lecture material, the other devoted to assignments.
How are students assessed
The bulk of the assessment is weekly programming assignments, which usually involve solving an interesting
application problem using an efficient algorithm learned in lecture. Students spend 10-20 hours per week on these
assignments and often consult frequently with section instructors for help.
• Monte Carlo simulation to address the percolation problem from physical chemistry, based on efficient
algorithms for the union-find problem.
• Develop generic data types for deques and randomized queues.
• Find collinear points in a point set, using an efficient generic sorting algorithm implementation.
• Implement A* search to solve a combinatorial problem, based on an efficient priority queue
implementation.
• Implement a data type that supports range search and near-neighbor search in point sets, using kD trees.
• Build and search a “WordNet” directed acyclic graph.
• Use maxflow to solve the “baseball elimination” problem.
• Develop an efficient implementation of Burrow-Wheeler data compression.
Exercises for self-assessment are available on the web, are a topic of discussion in sections, and are good
preparation for exams. A mid-term exam and a final exam account for a significant portion of the grade.
Course textbooks and materials
The course is based on the textbook Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne (Addison-
Wesley Professional, 2011, ISBN 0-321-57351-X). The book is supported by a public “booksite”
(http://algs4.cs.princeton.edu), which contains a condensed version of the text narrative (for reference while
online) Java code for the algorithms and clients in the book, and many related algorithms and clients, test data sets,
simulations, exercises, and solutions to selected exercises. The booksite also has lecture slides and other teaching
material for use by faculty at other universities.
A separate website specific to each offering of the course contains detailed information about schedule, grading
policies, and programming assignments.
Why do you teach the course this way
The motivation for this course is the idea that knowledge of classical algorithms is fundamental to any computer
science curriculum, but it is not just for programmers and computer science students. Everyone who uses a
computer wants it to run faster or to solve larger problems. The algorithms in the course represent a body of
knowledge developed over the last 50 years that has become indispensable. As the scope of computer applications
continues to grow, so grows the impact of these basic methods. Our experience in developing this course over
several decades has shown that the most effective way to teach these methods is to integrate them with
applications as students are first learning to tackle significant programming problems, as opposed to the oft-used
alternative where they are taught in a theory course. With this approach, we are reaching four times as many
students as do typical algorithms courses. Furthermore, our CS majors have a solid knowledge of the algorithms
when they later learn more about their theoretical underpinnings, and all our students have an understanding that
efficient algorithms are necessary in many contexts.
Body of Knowledge coverage
KA Knowledge Unit Topics Covered Hours
SDF Algorithms and Design Encapsulation, separation of behavior and implementation. 1
PL
Object-oriented
programming
Object-oriented design, encapsulation, iterators. 1
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