Course Details

Course Number: 01:198:344
Course type: Undergraduate
Semester 1: Fall
Semester 2: Spring
Credits: 4
Description:

To study a variety of useful algorithms and analyze their complexity; by that experience to gain insight into principles and data-structures useful in algorithm design.

Prerequisite information:

01:198:112; 01:198:206.

- A grade below a "C" in a prerequisite course will not satisfy that prerequisite requirement.

Course links: 01:198:112 - Data Structures, 01:198:206 - Introduction to Discrete Structures II
This course is a Pre-requisite for the Following Courses: 01:198:452 - Formal Languages and Automata
Topics:

Methods for expressing and comparing complexity of algorithms: worst and average cases, lower bounds on algorithm classes, verification of correctness. Application of such analysis to variety of specific algorithms: searching, merging, sorting (including quick and heap internal and Fibonacci external sorts); graph problems (including connected components, shortest path, minimum spanning tree. and biconnected components); language problems (including string matching and parsing).
Consideration of a number of hard problems: knapsack, satisfiability, traveling salesman problems.

Development of NP-complete classification and its consequence.

Approximation algorithms.

Expected Work: Regular exercises, (about) 6 small programs
Exams: Short quizzes, midterm and final exam (See Instructor's Class URL for more details)
Learning Goals:

Computer Science majors ...

  • will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
  • will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
  • will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).