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

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Lecture

 Section 1:  Milam 123   MWF 10:00 - 10:50

Instructor

 Paul D. Paulson    (contact info)

Office Hours

 MW  2:30 - 4:30 pm
TR  by appointment in Dearborn 303E

Recitations (weeks 2 - 10)

 Section 012:  T    11:30 - 12:20 in Owen 103
Section 013:  T    12:30 - 13:20 in Kidder 236

Teaching Assistants

Office hours in Hovland 108

Ying Kan   T R  2:00 - 4:00 pm

Peregrine Walther-Fisher   M W  2:00 - 4:00 pm

Prerequisites

 CS 161, MTH 231

Textbook

 Carrano, F. & Savitch, W., "Data Structures and Abstractions with Java", Pearson Education, 2003
ISBN # 0-13-017489-0
 

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.

Calendar

 Check here every week; the calendar is subject to "adjustments"

Grades
  • Homework ("recommended activities")
  • 4 programming projects @ 6.25%
  • 3 quizzes @ 5.0%
  • 2 midterm exams @ 17.5% (in class)
  • Final exam  (Monday, Dec. 6, 12:00 noon, MLM123)

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.
  • 0%
  • 25%
  • 15%
  • 35%
  • 25%
Academic Honesty Policy See the university, college, department, and course policies.
Obviously, compliance is expected.