Meeting Times and Locations

• Lecture: Monday, Wednesday, and Friday 1410-1500 in REID 401

Instructor

• Charlie Murphy: Office Hours are Monday, Wednesday, and Friday from 1510-1600 and by appointment in Barnard 344.

Textbook and Resources

• Mathematics: A Discrete Introduction by Edward A. Scheinerman, 3rd Edition.
• Alternative textbook: Discrete Mathematics with Applications by Susanna S. Epp, 4th Edition.
• Computer Science Help Center.
• Private tutors can be arranged through SmartyCats for $2/hour.

Catalog Description

This course covers logic, discrete probability, recurrence relations, Boolean algebra, sets, relations, counting, functions, maps, Big-O notation, proof techniques including induction, and proof by contradiction

Course Outcomes

1. Apply formal proof techniques, including mathematical induction and proof by contradiction.
2. Use algorithmic complexity to compare different program designs for a problem.
3. Solve problems that require logic, sets, combinations, permutations and functions.
4. Solve problems that require discrete probability.
5. Solve problems that use basic graph theory.

Graded Items

• Attendence - 10%.
  Experiences has shown that students who attend class regularly tend to perform better than students who do not. If you attend at least 80% of the days attendence are taken, you will earn the entire 10%. Otherwise, your percent will reflect your attendance rate when attendance is taken.
• Participation - 5%.
  Experience shows that students who actively participate in class perform better than students who do not. You are expected to answer questions and attempt solutions when called upon. It is normal and expected that you may not know every answer or solution; attempting is sufficient for full credit.
• Assignments - 30% (all weighted equally).
• Final Presentation - 25%.
• Final Take Home Exam - 30%

Grading Policy

Grades will be determined (after any curving takes place) based on your class average as follows:

• 93+: A
• 90+: A-
• 87+: B+
• 83+: B
• 80+: B-
• 77+: C+
• 73+: C
• 70+: C-
• 67+: D+
• 63+: D
• 60+: D-

Late Policy

It is expected that you will turn in homework in person at the beginning of class on the day the Assignment is due; if you will be absent the day an assignment is due, you may submit your homework by email before the start of the class the assignment is due.

Any assignment submitted after the due date will be given the grade of 0%.

For all assignments, you may re-submit the assignment a week following the return of graded assignments with corrections for up-to 50% of the remaining points. For example, if you originally scored 60% on an assignment, you may resubmit a corrected assignment for up-to 20% additional credit. If you scored 0% on an assignment (e.g., because it was not turned in on time), you may gain up-to 50% on the assignment when submitting it during the regrading period.

The final take home exam is to be turned in at the beginning of the last class meeting of the course. Failure to submit by the due date will result in a 0%. As with assignments, if under extenuating circumstances you cannot attend the final course meeting, you may submit the final take home exam via email before the final course meeting begins. The final take home exam cannot be re-submitted for re-grading.

Collaboration Policy

All students should read the MSU Student Conduct Code.

When it comes to assignments, you should do your own work but you may

• Work with other people in your group (if groups are allowed). Each assignment will specify the maximum number of people per team.
• Share ideas with people in other teams.
• Discuss high level proof and problem solving techniques.

If you discuss with someone from another team, you are expected to list each person and what you discussed for each problem of each assignment. You may not

• Copy anothers or share your own solution to assignments.
• Post assignments or problems to the internet.

Academic misconduct will result in an "F" for the course and being reported to the Dean of Students.

Generative AI Policy

Similar to the collaboration policy, students should treat interacting with Generative AI chatbots (e.g., chatgpt, claude, gemini) as interacting with another student in a separate team. You are expected to completely understand and come-up with solutions yourself (or with your team if applicable).

When it comes to assignments, you may

• Discuss high level proof and problem solving techniques.
• Seek solutions to similar but distinct problems.

When you interact with a generative AI on an assignment, you must cite which generative AI chatbot you interacted with for each problem on each assignment. Additionally, you should provide what was discussed with the chatbot and/or provide the prompts used. You may not

• Prompt a generative AI chatbot/Agent to solve a problem for you.
• Post the entire assignment or problem to the AI chatbot/Agent.

Academic misconduct will result in an "F" for the course and being reported to the Dean of Students.

Course Calendar

Schedule is subject to change

---

Last modified: January 14, 2026