CSE 2331
Transcript Abbreviation:
Fndns 2: DS & Alg
Course Description:
Design/analysis of algorithms and data structures; divide-and-conquer; sorting and selection, search trees, hashing, graph algorithms, string matching; probabilistic analysis; randomized algorithms; NP-completeness.
Course Levels:
Undergraduate (1000-5000 level)
Designation:
Required
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
7 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Lima
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 2231, 2321, and Stat 3460 or 3470, and enrollment in CSE, CIS, ECE, Data Analytics, Math majors or CIS minor. Concur: Math 3345.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 5331.
Course Goals / Objectives:
Be competent with using asymptotic notation
Be familiar with designing graph algorithms
Be familiar with designing and analyzing divide-and-conquer algorithms
Be familiar with the use of balanced trees
Be familiar with hashing
Be familiar with heaps
Be familiar with designing backtracking algorithms.
Be familiar with string matching.
Be exposed to selection algorithms
Be exposed to probabilistic algorithms
Be exposed to formal languages and finite automata
Be exposed to NP-completeness
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Design and analysis of recursive algorithms | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Balanced trees and heaps | 9.0 | 0.0 | 0 | 0.0 | 0 |
| Hashing | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Graph algorithms | 12.0 | 0.0 | 0 | 0.0 | 0 |
| Backtracking algorithms | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Sorting and selection | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 20% |
| Classroom participation | 10% |
| Midterms, final | 70% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Introduction to Algorithms | Cormen, Leiserson, Rivest and Stein |
ABET-CAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| Substantial contribution (3-6 hours) | 2 | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| Some contribution (1-2 hours) | 4 | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| Substantial contribution (3-6 hours) | 6 | Apply computer science theory and software development fundamentals to produce computing-based solutions. |
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Substantial contribution (3-6 hours) | 2 | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| Some contribution (1-2 hours) | 4 | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| Some contribution (1-2 hours) | 6 | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| Substantial contribution (3-6 hours) | 7 | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info: