CS 361 Class Syllabus

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Course Description

Text:

What you should know

• concept of abstract data type (ADT)
• good programming skills in C, C++, or Java
• 2 semesters of calculus (infinite sums and limits, derivatives, etc)
• CS201
• CS251L
• Math 163L

Assignments:

• Assignments are due at the beginning of class on the due date. Assignment deadlines are strict: late homeworks will automatically receive a grade of zero, without prior approval (easy for one, harder for two, very difficult for three or more).
• Group collaboration is encouraged on all the homeworks, provided that you write at the top of your homework the names of all the other students that you collaborated with. Note that although collaboration is encouraged on homeworks, the solutions must always be written up individually, and you should not look at or copy another student's solution. Exchanging homework solutions is cheating. In case two or more students present essentially identical solutions, the students will receive a 0 on the assignment, will be reported to the University Administration and may not be permitted to continue in the class.
• Put pages of hw in order. We don't care what order you solve the hw in, but before you turn it in, you must put the problems in order (this makes grading easier)
• Staple hws, do not use paper clips, folding, tape, putty, gum, etc. Prof Saia and the TA do not bring staplers to class, so make sure you staple the hw before class (stapling helps us keep together all pages of your hw)
• Regrades: if you feel a mistake was made grading your hw, please first let the TA know about it - if you still feel there is a problem, then please talk to Prof. Saia. Please ask for a regrade within one week of receiving the graded assignment.

Notes on Grading Hws

• Clarity: Make sure all of your work and answers are clearly legible and well separated from other problems. If we can't read it, then we can't grade it. Likewise, if we can't immediately find all of the relevant work for a problem, then we will be more likely to grade only what we see at first.
• Completeness: Full credit for all problems is based on both sufficient intermediate work (the lack of which often produces a 'justify' comment) and the final answer. There are many ways of doing most problems, and we need to understand exactly how YOU chose to solve each problem. Here is a good rule of thumb for deciding how much detail is sufficient: if you were to present your solution to the class and everyone understood the steps, then you can assume it is sufficient.
• Succinctness: The work and solutions which you hand-in should be long enough to convey exactly why the answer you get is correct, yet short enough to be easily digestible by someone with a basic knowledge of this material. If you find yourself doing more than half a page of dense algebra, generating more than a dozen numeric values or using more than a page or two of paper per problem for your solution, you're probably doing too much work. Don't turn in pages with scratch work or multiple answers - if you need to do scratch work, do it on separate scratch paper. Clearly indicate your final answer (circle, box, underline, etc.). Note: It's usually best to rewrite your solution to a problem before you hand it in. If you do this, you'll find you can usually make the solution much more succinct.

Topics

• Asymptotic Analysis and Big-O notation. These are mathematical tools for analyzing the running time of algorithms and data structures. We'll also review logarithms (1 week, Chapter 3 in text)
• Recurrence Relations: How to formulate them and how to use annihilators and change of variable transformations to solve them. Recurrence relations are mathematical tools for analyzing the performance of algorithms. (2-3 weeks, Chapter 4 in text)
• Sorting algorithms: Bubble sort, merge sort(divide and conquer) and quicksort(worst vs. average case behavior), loop invariants, and proofs of correctness. Time permitting, we'll discuss various selection algorithms. (2 weeks, Chapters 7 and 8)
• Probability and Expectation: Basic probability theory, linearity of expectation, birthday paradox, coupon collector's problem, average case analysis of quicksort. Probability theory is another mathematical tool for analysis or randomized algorithms. (2 weeks, Chapter 5)
• Heaps and Priority Queues: ADT's and implementations, Linked List versus Binary heap. (1 week, Chapter 6)
• Binary Search Trees: Detailed descriptions and analyses of skip lists and AVL trees (p. 296 in book). High level descriptions of splay trees, red-black trees and B-trees. (2 weeks, Chapter 12 and p. 296)
• Hash Tables: Insert, find and delete in O(1) time, use of pseudo-randomness to get good hash functions. (1 week, Chapter 11)
• Graph Algorithms (time permitting): Shortest paths problem, Dijkstra's algorithm, minimum spanning tree problem and Prim's and Kruskall's algorithms, proofs of correctness of Prims and Kruskalls. (2 weeks, Chapters 22 and 23)

Course Assessment

• Homeworks 20% (about 6-8)
• Class Project 20%
• Midterm 20%
• Final 30%
• Class Participation, 10% . I expect about 90% attendance in class. There will be frequent in-class exercises, and I will call on a random person in the class to answer. The participation grade will be based on a check system:
  • If I call on you for an in-class exercise, and you give an educated guess, ask me an intelligent question, or give a partial answer, you get one check
  • If I call on you for an in-class exercise, and you give a correct or near-correct answer, you get two checks
  • If you come to my office hours or the TA's, you get one check (good for one time only)
  The more checks you have, the better your participation grade.

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