Advanced Algorithms (CSCI 432)

Table of Contents

  1. Short Description of Course
  2. When and Where?
  3. Land Acknowledgement
  4. Diversity Statement
  5. Course Outcomes and Objectives
  6. Grading
  7. Special Needs Information
  8. Policies
    1. Policy on Homework
    2. Policy on Collaboration
    3. Policy on Chat GPT
    4. Policy on Classroom Etiquette
    5. Policies for COVID-19 and Other Illnesses
    6. Policy on Withdrawing
  9. MSU Policies
    1. Academic Integrity
    2. Plagiarism and Cheating
    3. MSU Drug and Alcohol Policies
  10. Resources
    1. Technical Resources
    2. Course Textbook
  11. Use of Materials

Short Description of Course

A rigorous examination of advanced algorithms and data structures.

When and Where?

When? MWF 15:10-16:00 Where? Roberts 218

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.

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.

Grading

Your grade for this class will be determined by:

  • 40% Homework and In-Class Assignments
  • 20% Project

  • 40% Exams

  • Homework: All assignments must be submitted by 23:59 on the due date. Late assignments will not be accepted. The lowest homework grade will be dropped. The submission should be typeset in LaTex using the provided template, and submitted as a PDF both in D2L and Gradescope.
  • Project: Groups will be assigned. More details to come. All written deliverables are expected to be submitted in PDF form.
  • Exams: We will have four exams in this course. Each exam will be 10% of the grade. The final grade in the class is lower-bounded by 10-points above your second lowest exam grade..
  • 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+).

Special Needs Information

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.

Policies

Policy on Homework

Unless specifically allowed for an anssignment, do not search for answers to the problems. You will learn in this class by solving the problems, not by reading the solutions. Regurgitating solutions you found elsewhere (including Chat GPT) will not help you learn the material. If you feel that you need additional resources, please ask.

Policy on Collaboration

Collaboration is encouraged on all aspects of the class, except where explicitly forbidden. Note:

  • All collaboration (who and what) must be clearly indicated in writing on anything turned in.
  • Homework may be solved collaboratively except as explicitly forbidden, but solutions must be written up independently. This is best done by writing your solutions when not in a group setting. Groups should be small enough that each member plays a significant role. (Note, if there is a group assignment, each group is treated as an ‘individual’).

Policy on Chat GPT

TODO: discuss

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 15 October 2023, 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. Since this class involves a project, the decision to withdraw must also be discussed with your group (after groups are formed).

MSU Policies

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.

Resources

Technical Resources

Course Textbook

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.