COT 3400 Design & Analysis of Algorithms

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Florida Gulf Coast University
U.A. Whitaker College of Engineering
Department of Computing & Software Engineering

Course Catalog Description:

The design, implementation, analysis, and application of a range of computer algorithms are explored. Function order of growth and amortized analysis are used in analyzing algorithms. A review and extension of data structure topics including trees and graphs are covered. Algorithm design strategies such as divide-and- conquer, the greedy method, and dynamic programming are studied.

Prerequisites:

COP 3530 for level Undergraduate with minimum grade of C

Required Material

Textbook:

Cormen et. al.,Introduction to Algorithms, The MIT Press; third edition ISBN 10: 02620 33844, ISBN 13: 9780262033848

Learning Outcomes:

  1. Acquired basic proficiency in designing and implementing algorithms with standard techniques such as divide and conquer, dynamic programming, and greedy approach
  2. Expand their knowledge in advanced data structures on trees and graphs
  3. Acquired skills in analyzing the computational complexity and its applicability context
  4. Acquired fundamental knowledge of NP Completeness, the associated methodology, and their relevance
  5. Introduced to the domain of approximate algorithms

Instructional Methods:

This is a face-to-face course with lectures, instructor led programming examples, and in-class labs.

Attendance:

Attendance is mandatory for all classes. Attendance will be taken and will count towards participation credit. Students should notify me at least 48 hours in advance of intent to miss a class to arrange alternate schedules for in-class assignments.

Communication:

All course related communication should be via Canvas message. Each student is responsible for checking his/her FGCU email at least once a day. The instructor will respond to your emails within 24 hours except weekends and holidays.

Topics / Tentative Schedule:

  • Module 1 - Introduction & Fundamentals
  • Module 2 - Divide & Conquer
  • Module 3 - Sorting & Heaps
  • Module 4 - Hashing & Search Trees
  • Module 5 - Dynamic Programming & Greedy
  • Module 6 - Graph Algorithms & Beyond

Assessment:

There is no grace period for late submission of assignments. All assignments with due dates and times will be posted on Canvas. Alternate schedules can be arranged ahead of time with instructor approval.

Working Independently:

Students are to work independently on all non-group assignments. It is cheating to see other classmate's work or let other classmates see yours. This does not mean you cannot assist or discuss assignments with your classmates.

*** IMPORTANT ***

Student submissions that contain work from another student or source will receive a zero for that assignment. A second occurrence will result in failing the class, and the academic sanction will be reported to the Dean of Students.

Generative AI Use Policy:

Use of Generative AI tools (such as ChatGPT, Copilot, Gemini, etc.) is permitted in this course, but only within strict boundaries that support learning without compromising academic integrity or the intent of the assignments.

Permitted Use:

  • Generative AI may be used to assist with understanding a specific concept, algorithm, or section of provided code.
  • Prompts must be narrowly focused (for example, explaining how an algorithm works or clarifying what a provided code segment does).
  • You may include code from the textbook, lecture materials, or other sources in a prompt only for the purpose of understanding it.
  • AI may be used to clarify or reinforce understanding, but not to generate code or complete any portion of an assignment.

Prohibited Use:

  • Do not prompt with entire assignments, assignment questions, or large portions of an assignment.
  • Do not use AI to generate code that is submitted as part of your work.
  • Do not use AI to debug, improve, or modify code that you are submitting.

Disclosure Requirement:

  • You must submit a copy of all AI prompts and corresponding responses along with your assignment, including coding assignments.
  • Submissions without accompanying AI usage documentation will be treated as not using AI.

Violations:

  • First violation: zero on the assignment.
  • Second violation or use of AI beyond what was disclosed may result in a formal academic integrity referral.

This policy is intended to support learning while ensuring that submitted work reflects your own understanding and effort.

Assignments (50%):

Individual problems and small programs worked outside of class.

Exams (35%):

Written exams taken in class.

Participation (15%):

In class quizzes and labs, attendance, and overall participation.

Grading

GRADE GPA Range
 A  4.00 93 - 100
 A-  3.75 90 - 92
 B+ 3.25 87 -  89
 B  3.00 83 -  86
 B- 2.75 80 -  82
 C+ 2.25 77 -  79
 C  2.00 70 -  76
 D  1.00 60 -  69
 F  0.00  0 -  59

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