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Math 381 Summer 2013
Discrete Mathematical Modeling
Course Syllabus

Description/Subject Material: This course is an introduction to methods of discrete mathematics, including topics from graph theory, combinatorics, probability, and optimization. Specifically, we will cover topics from linear and integer programming, graph theory, Markov Chains, Monte Carlo simulation, and network flows.

Textbook: There is no required textbook for this course. I recommend using Mathematical Modeling by Wayne L. Winston as a reference; it is available at the Bookstore and is on hold at the library. We will also draw extensively from the old course notes. These notes have been developed by many different professors over the past years. I will add notes and resources as necessary.

Lecture: Attending the lecture is a fundamental part of the course; you will be responsible for material presented in lecture regardless of whether it is discussed in the textbook. As the course does not use a book exclusively, it's very important to attend lectures so that you know what we went over.

Classroom Conduct: In the classroom, a certain level of respect and attentiveness is expected. Please do not use phones, play games, or talk to friends during lecture. This can be distracting to other students and the instructor.

Homework: Homework problems will be assigned on the course homework page, and should be completed and turned in by the beginning of class on the indicated due date. You should make every effort to complete the homework assignments and seek help with problems you have been unable to solve. Homework will often be discussed in class and collected, although not every problem may be graded in detail. You are welcome to work in small groups on homework, but you must each write up the assignments separately, and mark your work as collaborative.

Quizzes: There will be periodic quizzes given on Catalyst. Each quiz will be open for 24 hours, and you will have advance warning of the date/time of availability. There are no make-up quizzes, so you should be sure to take the quiz during the testing period.

Course Project: A major feature of this course is the development of a project to be handed in at the end of the quarter. These modeling projects are to be completed in groups of up to three students. See the project information sheet for details and due dates.

Class participation: It is essential that you come to class every lecture. Much of what we discuss is not in the ext, and we will often spend time working together or in small groups to develop models. You will often be given ideas to think about between lectures, and it is expected that you will come to class prepared to participate in the conversation and contribute to the discussion.

Grading: Your final course grade will be based on the following weighted average: A curve may be applied to final scores or individual examinations at the instructor's discretion.

Writing assignments: Students in Math 381 earn W-credit for the writing assignments in this class. The course project will be graded in part on the writing style and clarity of explanations. You should expect many problems in homework and quizzes that require writing descriptions of modeling problems or algorithms.

Computer programming: You need not do extensive computer programming in this course, but it is often useful for solving interesting problems or developing new techniques. I will give examples in class using the R programming language. This is a very straightforward and convenient language for testing simple algorithms and analyzing data. It is commonly used in statistical applications in the "real" world, and best of all, it's FREE! You're welcome to use other languages if you prefer for your final project or any other course assignments.

Other resources: As mentioned, this course does not use a book strictly. If you need additional resources on a given topic, I encourage you to go to the library or the internet to find some books or articles. If you're struggling with that, you may ask me for help--but I will not offer resources unless you can show that you've already done some digging yourself.

Academic Honesty: Academic dishonesty is a serious offense, carrying serious administrative sanctions. Any instance of dishonesty will be pursued by the instructor. It is in your best interest to follow all policies laid out here and elsewhere on the website, and familiarize yourself with the university guidelines for academic honesty. Please help maintain both your own integrity and the integrity of the University of Washington.

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