Discrete Mathematics & Functional Programming

CS/MATH 220, Whitman College, Fall 2016
MWF 11:00-11:50, Olin 124
Instructor: Janet Davis (davisj@whitman.edu)
Office hours: As posted and by appointment
Lab hours

About this course

Welcome to CS/MATH 220! The official course description:

Students will practice formal reasoning over discrete structures through two parallel modes: mathematical proofs and computer programs. We will introduce sets and lists, Boolean logic, and proof techniques. We will explore recursive algorithms and data types alongside mathematical and structural induction. We consider relations and functions as mathematical objects built on set theory and develop idioms of higher-order programming. If time permits, additional topics may include graphs, lattices, or groups, and their applications to computer science.

I am excited to bring together so many beautiful ideas in this course. I am experienced at teaching functional programming in Scheme to beginners. However, before this summer I had not formally studied discrete mathematics since I was an undergraduate myself, nor had I used the programming language SML. I look forward to learning together.

Course goals

By the end of this class, you will be able to:

Recurring themes include:


For a detailed outline, see the table of contents in your textbook. See also the course schedule.

Set and List.
We meet the basic mathematical concepts of set, element, set operations, cardinality, Cartesian product, and powerset. We learn the basics of the ML programming language including functions and datatypes. We learn to use ML's main composite type, the list.
We explore the system of Boolean logic (the "first-order predicate calculus"). This heading is characterized by three "games" to exercise your understanding of symbolic logic: 1. verifying logical equivalences; 2. verifying argument forms; 3. verifying argument forms with quantification. We also write ML programs that use Boolean operators and consider how the quantification of a problem specification affects the algorithm to solve it.
We learn to write careful proofs of set-theoretical propositions. This includes one of the most challenging sections, proofs about powersets. We also consider the connections between proofs and algorithms.
We build on our understanding of sets to consider the definition of mathematical relations and their properties, propositions about them, and programs that manipulate them. The relation is a useful concept in itself, but this heading also lets us practice further the proof and programming techniques we have learned so far.
Earlier parts of our study will have introduced recursive definitions, but here we tackle the idea head-on. Specific topics are recursive types in ML programming and proofs using structural and mathematical induction.
We study functions as mathematical objects built on set theory. These proofs will build on all the techniques you have learned so far. We also learn programming idioms that build on the theory of functions as mathematical objects.

Janet Davis (davisj@whitman.edu). Course goals and overview adapted from Thomas VanDrunen's syllabus for CS/MATH 243, Wheaton College.

Created August 28, 2016
Last revised August 30, 2016, 01:22:45 PM PDT
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.