Advanced Algorithms (CSCI 432)

Table of Contents

  1. Class Meetings
  2. Description
  3. Prerequisites
  4. Course Outcomes and Objectives
  5. Land Acknowledgement
  6. Diversity Statement
  7. Accomodations
  8. Grading
    1. Quizzes and Exams
    2. Homework
    3. Project
    4. Attendance
  9. Course Policies
    1. Policy on Classroom Etiquette
    2. Policies for COVID-19 and Other Illnesses
    3. Policy on Withdrawing
    4. Academic Integrity
    5. Plagiarism and Cheating
    6. MSU Drug and Alcohol Policies
  10. Useful Links
  11. Course Textbook
  12. Use of Materials

Welcome to Advanced Algorithms (CSCI 432), Spring 2026, taught by Prof. Brittany Terese Fasy. This website will serve as the course syllabus. Please read through.

Class Meetings

When? T,R 12:15-13:30
Where? Romney 315

Description

MSU Course Catalog Description:

A rigorous examination of advanced algorithms and data structures. Topics
include average case analysis, probabilistic algorithms, advanced graph problems
and theory, distributed and parallel programming.

From the Instructor:

This course is NOT a programming class, and is not structured like the 132 and
232 courses that precede it.  In this course, we will do many proofs (especially
using induction), and will be writing pseudo-code, not code.

Prerequisites

  • CSCI 246 (Discrete) or M 242 (Methods of Proof)
  • CSCI 232 (Data Structures and Algorithms)

In particular, a student enrolled in CSCI 432 should be familiar with:

  • sorting and searching algorithms
  • big-O notation,
  • basic recurrence relations,
  • heaps, queues, lists, and hash tables
  • proof by induction and by contradiction
  • discrete probability.

This is not an exhaustive list of prerequisite topics.

Course Outcomes and Objectives

This course introduces students to the analysis and design of computer algorithms. In this course, students will:

  • Analyze asymptotic time and space complexity of algorithms.
  • Describe algorithmic design paradigms (including dynamic programming, greedy algorithms, divide and conquer) and explain when an algorithmic design situation calls for it.
  • Apply methods of analysis (prove correctness, time/space complexity, termination) to new problems.
  • Use and analyze major graph algorithms and data structures.

Land Acknowledgement

Living in Montana, we are on the ancestral lands of American Indians, including the 12 tribal nations that call Montana home today: A’aninin (Gros Ventre), Amskapi/Piikani (Blackfeet), Annishinabe (Chippewa/Ojibway), Annishinabe/Métis (Little Shell Chippewa), Apsáalooke (Crow), Ktunaxa/Ksanka (Kootenai), Lakota, Dakota (Sioux), Nakoda (Assiniboine), Ne-i-yah-wahk (Plains Cree), Qíispé (Pend d’Oreille), Seliš (Salish), and Tsétsêhéstâhese/So’taahe (Northern Cheyenne). We honor and respect these tribal nations as we live, work, learn, and play in this state.

To learn more about Montana Indians, I suggest starting with the following pamphlet: Essential Understandings Regarding Montana Indians

Diversity Statement

Montana State University considers the diversity of its students, faculty, and staff to be a strength and critical to its educational mission. MSU expects every member of the university community to contribute to an inclusive and respectful culture for all in its classrooms, work environments, and at campus events. Dimensions of diversity can include sex, race, age, national origin, ethnicity, gender identity and expression, intellectual and physical ability, sexual orientation, income, faith and non-faith perspectives, socio-economic status, political ideology, education, primary language, family status, military experience, cognitive style, and communication style. The individual intersection of these experiences and characteristics must be valued in our community.

If there are aspects of the design, instruction, and/or experiences within this course that result in barriers to your inclusion or accurate assessment of achievement, please notify the instructor as soon as possible and/or contact Disability Services or the Office of Institutional Equity.

Accomodations

If you have a documented disability for which you are or may be requesting an accommodation(s), please contact both me and the office of Disabled Student Services before the second class meeting.

Grading

Your grade for this class will be determined by:

  • 5% In-class Assignments (Participation-based, not graded)
  • 30% Quizzes (Bi-Weekly on Tuesdays)
  • 45% Exams
  • 10% Homework
  • 10% Project

Quizzes and Exams

Quizzes are based on the reading for the class period in which the quiz is taken, or before.

We will have three exams in this course. Each exam will be 15% of the grade. See the schedule for the dates of the exams.

Homework

Each class period includes suggested reading and sometimes additional problems. The homework is a reflection of the effort that you put into working on the problems that appear in the reading, as well as any additional problems posted. Homework must be typeset in LaTex using the provided template. The first and last homework have a slightly different format than the rest.

Specifically, the homework problems you are expected to be working on are:

  1. All odd-numbered problems in the assigned readings.
  2. Any in-class problems not completed during class.

When working on homework problems, it is highly suggested that you do not search for answers (either on Google or on ChatGPT or any other means). You have the tools you need in order to solve all problems, without any assistance other than your course textbook and prerequisite knowledge. Often, it is helpful to find a homework buddy so that you can work on problems together. If you use other tools, it is doing yourself a disservice. Remember, you’ll need to know HOW to solve these problems in the end, for job interviews and such …

Project

Groups will be assigned. See Project for more details.

Attendance

Class attendance and participation is required and expected. If you consistently miss class, then your final grade may be dropped one letter grade (e.g., from B+ to C+).

Course Policies

Policy on Classroom Etiquette

Except for note taking and group work requiring a computer, please keep electronic devices off during class, as they can be distractions to other students. Disruptions to the class will result in being asked to leave the lecture, and one half-point will be deducted from your final course grade.

Policies for COVID-19 and Other Illnesses

Please be curteous to your fellow classmates and do not come to class if you are ill. If notified at least 24 hours in advance (and sometimes with less advance notice), you may join the lecture via Zoom if needed. If you need to miss class to quarantine or isolate due to COVID-19 (or for any other reason), please communicate with the instructor as soon as possible in order to coordinate a plan for making up the missed classwork. As a reminder, attendance is required. If the instructor is unable to make it to class, either a substitute will be found for those lectures or the class will be held via Zoom.

Policy on Withdrawing

After the Last Day to Drop Without a W Grade deadline has passed, I will only support requests to withdraw from this course with a “W” grade if extraordinary personal circumstances exist. If you are considering withdrawing from this class, discussing this with me as early as possible is advised. Because this class involves a project, the decision to withdraw must also be discussed with your group (after groups are formed).

Academic Integrity

By participating in this class, you agree to abide by the Student Code of Conduct. This includes the following academic expectations:

  • Be prompt and regular in attending classes;
  • Be well-prepared for classes;
  • Submit required assignments in a timely manner;
  • Take exams when scheduled, unless rescheduled under 310.01;
  • Act in a respectful manner toward other students and the instructor and in a way that does not detract from the learning experience; and
  • Make and keep appointments when necessary to meet with the instructor.

Plagiarism and Cheating

Plagiarism and cheating will not be tolerated.

The integrity of the academic process requires that credit be given where credit is due. Accordingly, it is academic misconduct to present the ideas or works of another as one’s own work, or to permit another to present one’s work without customary and proper acknowledgment of authorship. Students may collaborate with other students only as expressly permitted by the instructor. Students are responsible for the honest completion and representation of their work, the appropriate citation of sources and the respect and recognition of others’ academic endeavors.

Plagiarism will not be tolerated in this course. According to the Merriam-Webster dictionary, plagiarism is “the act of using another person’s words or ideas without giving credit to that person.” Proper credit means describing all outside resources (conversations, websites, etc.), and explaining the extent to which the resource was used. Penalties for plagiarism at MSU include (but are not limited to) failing the assignment, failing the class, or having your degree revoked. This is serious, so do not plagiarize. Even inadvertent or unintentional misuse or appropriation of another’s work (such as relying heavily on source material that is not expressly acknowledged) is considered plagiarism.

MSU Drug and Alcohol Policies

Per the Code of Conduct for students, no student may come to class under the influence of drugs or alcohol, as that would not be “fostering a healthy, safe and productive campus and community.” See Alcohol and Drug Policies Website for more information.

Please note that Dr. Fasy is a mandatory reporter per the Cleary Act.

Course Textbook

  • (Required) Algorithms by Jeff Erickson, abbreviated JE
  • (Suggested) Introduction to Algorithms, Third Edition by Cormen, Leiserson, Rivest, and Stein, abbreviated CLRS.
  • (Suggested) Building Blocks for TCS by Fleck for a refresher on 200-level course material, abbreviated MMF.

Use of Materials

This syllabus, course lectures and presentations, and any course materials provided throughout this term are protected by U.S. copyright laws. Students enrolled in the course may use them for their own research and educational purposes. However, reproducing, selling or otherwise distributing these materials without written permission of the copyright owner is expressly prohibited, including providing materials (or solutions) to commercial platforms such as Chegg or CourseHero. Doing so constitutes a violation of U.S. copyright law as well as MSU’s Code of Student Conduct. Instructors from other universities are free to borrow the material on this website for use in their own classrooms.