The schedule below shows the tentative dates for all class topics, readings, and assignments. You should complete all assigned reading before class on the day it is listed. Labs will be available shortly before the assigned lab day. There may be some revisions to the schedule during the semester, but I will make sure to announce these changes in class. If you view this page with JavaScript enabled you can jump to the current week on the schedule, and you should see the next day of class highlighted in the schedule below.
We begin the class by exploring the definition of computer science and by trying to write some basic algorithms.
We begin to consider the content and structure of the course. We also prepare ourselves to use our laboratory environment (that is, the Linux workstations and the DrRacket Programming Environment).
We explore some of the basic types in Scheme and the operations available for those types.
We start to explore Scheme’s list data structure and some ways to use lists to work with collections of data.
We consider new ways to write your own procedures and why you might do so.
We explore the whys and hows of working with others. We also catch up on any confusing issues from the first few days of class.
We consider documentation for your programs: Why to write documention, when to write documentation, how to write documentation. We also explore the 6P style of documentation that we use in this course.
We consider testing When, why, and how you might test the procedures and programs that you write.
We consider how to deal with compound data, such as the title, latitude, longitude, time, and date of an event.
We consider a variety of techniques for gathering lists and tables of data from files.
We consider a new type and its use in selecting elements from lists.
We explore techniques for displaying simple kinds of data, such as coordinate pairs or counts of categorical data.
We revisit preconditions. We then consider programming techniques for ensuring that preconditions are met.
We begin our exploration of recursion, the most general form of repetition available in Scheme. You can use recursion to both build and iterate over different kinds of values.
We consider a different form of recursion, one based on the construction of recursive helper procedures that take additional parameters. Along the way, we consider the idea of tail recursion. We also explore how careless design of recursive procedures can inadvertently lead to slow execution.
We conclude our initial forays into list recursion by looking for some common patterns in the design of recursive procedures.
We consider a slightly different kind of recursion, numeric recursion. In this technique, we once again have procedures call themselves. However, the parameter that we “simplify” at every step is a number, rather than a list.
We explore how and why one writes local recursive procedures.
We explore techniques for understanding and correcting flaws in our programs.
We consider Scheme’s random
procedure and how one might use that
procedure in writing simple simulations.
We consider pairs, the basic data type used to build lists and other structures in Scheme. We also consider why it is useful to understand about pairs.
We consider vectors, an alternative to lists for storing collections of data.
We revisit files, considering the lower-level operations for working with files, a technique for structuring information that permits the information to persist across invocations of Scheme. Files also let our Scheme programs share information with other programs.
We revisit the topic of higher-order procedures, one of the most
important techniques in languages like Scheme. Higher-order procedures
are procedures – like map
, left-section
, or compose
– that take
other procedures as parameters, return other procedures as values, or
both.
We consider trees, structures built from pairs. Trees are somewhat like two-dimensional lists.
We explore techniques for analyzing the number of calls made in evaluating procedures, particularly recursive procedures. We consider why such analysis is useful.
We consider association lists, a simple, but useful, technique for organizing tables of information.
We introduce the project.
We provide time for groups to work on their projects.
We consider the general problem of searching. We explore binary search, one of the most efficient algorithms for searching.
We explore the problem of sorting. When you sort a list, vector, or other collection, you put the elements in order. The order of the elements usually corresponds to the type of the elements. We might sort strings alphabetically, grades numerically, colors by brightness, and so on and so forth.
We provide additional time for groups to work on their projects.
We move from our general exploration of sorting to the implementation of a particular sorting algorithm, insertion sort. We also explore how the running time for that algorithm varies based on the number of values we are sorting.
We will have some additional time to work on projects before the project deadline.
We continue our exploration of sorting by considering the applicability of the divide-and-conquer approach to the problem of sorting. We look at one particular divide-and-conquer algorithm, merge sort. We explore how the running time for that algorithm varies based on the number of values we are sorting.
We explore your projects.
We conclude the course by considering the topics we’ve covered, and discuss the concepts you will see in future CS courses.