CS 162   (4 credits)
Introduction to Computer Science II
Spring 2004

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Lecture Section 1:  Cord 1109   MWF 9:00-9:50
Instructor
(contact info)		 Prof. Timothy A. Budd
Office Hours MWF 10:30-12:00, Dearborn 218
Recitations Section 5:  Tues    8:30 - 9:20 in App 101 (Wallace)
Section 6:  Tues    9:30 - 10:20 in App 101 (Neumann) 
Section 7:  Tues    11:30 - 12:20 in Covl 218 (Parker)
Section 8:  Tues     12:30 - 1:20 in Covl 218 (Maki)
Teaching Assistant

Office hours in Hovland 108

 Alec Maki : Weds 12:30-2:20, Thurs 1:30-3:20
Christoph Neumann: Mon 1:30-3:30
Charles Parker: Tues, Wed 4-6
James Wallace: Tues 2-4
Prerequisites CS 161, MTH 231
Textbook Horstmann, Cay, Big Java, Wiley, 2002
Course Learning Objectives 1.  Design and implement programs that require the use of multiple classes and structures, requiring the understanding of abstraction, modularity, separation of concerns, and exception handling.
2.  Implement abstract data types using classes, objects, encapsulation, inheritance, and polymorphism.
3.  Determine the average-case and worst-case complexity for moderately complicated algorithms in these complexity classes: O(1), O(log n), O(n), O(n log n), and O(n2).
4.  Develop test-data sets and testing plans for programming projects.
5.  Given a problem specification, select the correct linear structure (array, stack, queue, singly-linked list, or doubly-linked list).  Given two linear structures, describe the relative advantages and disadvantages of each.
6.  Given intermediate-level problems involving repetition, choose appropriately between an iterative and recursive algorithm.  Describe the relative advantages and disadvantages of recursion versus iteration.
Schedule Check here every week; the schedule is subject to "adjustments"
Grades
  • 5 homework sets @ 3.0% 
  • 6 programming projects @ 6.25% 
  • 3 quizzes @ 5.0% 
  • 2 midterm exams @ 12.5% (in class) 
  • Final exam  (Tuesday, June 8, noon, Cord 1109)
Final grades are based on the accumulated percentage.  See the evaluation criteria and grading scale.  Quiz, exam, and final grades may be adjusted linearly if it seems appropriate.
  • 15% 
  • 25% 
  • 15% 
  • 25% 
  • 20%

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Academic Honesty Policy See the university, college, department, and course policies.
Obviously, compliance is expected.