EECS 114 Engineering Data Structures and Algorithms (2016-2017)

EECS 114 Engineering Data Structures and Algorithms

(Required for CpE.)
Catalog Data:

EECS 114 Engineering Data Structures and Algorithms (Credit Units: 4) Introduces abstract behavior of classes data structures, alternative implementations, informal analysis of time and space efficiency. Also introduces classic algorithms and efficient algorithm design techniques(recursion, divide-and-conquer, branch-and-bound, dynamic programming). Prerequisite: EECS40. Computer Engineering majors have first consideration or enrollment. (Design units: 2)

Required Textbook:
. Edition, , 1969, ISBN-13 978-0262033848.

Recommended Textbook:
None
References:
None
Coordinator:
Brian Demsky
Relationship to Student Outcomes
No student outcomes specified.
Course Learning Outcomes. Students will:

1. Implement the algorithms and data structures in a high-level programming language.

2. Analyze the time and space complexity of each of the data structures and algorithms covered in this class and for compositions of functions with known complexities.

Prerequisites by Topic

Object-oriented programming.

Lecture Topics:
  • Linked Lists, Stacks, Queues
  • Binary Search Trees, Binary Heaps
  • Hash Tables
  • Graphs
  • Sequential search
  • Binary search
  • Insertion sort
  • Merge sort
  • Quick sort
  • Depth-first search and breadth-first search for graphs
  • Minimum spanning tree of graphs
  • Shortest path algorithms: Bellman-Ford and Dijkstra
  • Branch-and-bound, dynamic programming
Class Schedule:

Meets for 3 hours of lecture and 2 hours of laboratory each week for 10 weeks.

Computer Usage:

SecureCRT and XWin32 for logging into the UNIX servers.

Laboratory Projects:

Weekly or biweekly programming assignments of moderate difficulty.

Professional Component

Contributes toward the Computer Engineering Topics Courses and Major Design experience.

Design Content Description
Approach:

Students implement and measure the performance of alternative designs to determine time complexity by empirical means.

Lectures: 20%
Laboratory Portion: 80%
Grading Criteria:
  • Homework: 30%
  • Midterm exam: 30%
  • Final exam: 40%
  • Total: 100%
Estimated ABET Category Content:

Mathematics and Basic Science: 0.0 credit units

Computing: 0.0 credit units

Engineering Topics: 4.0 credit units

Engineering Science: 2.0 credit units

Engineering Design: 2.0 credit units

Prepared:
July 12, 2016
Senate Approved:
April 29, 2013
Approved Effective:
2013 Fall Qtr