CS312 is the third programming course in the Computer Science curriculum, following CS100 and CS211. The primary goal of the course is to give students a firm foundation in the fundamental principles of programming and computer science. Consequently, CS312 covers a broad set of topics including (1) alternative programming paradigms (beyond imperative and object-oriented programming), (2) key data structures and algorithms, (3) reasoning about program behavior and complexity, (4) type systems and data abstraction, and (5) the design and implementation of programming languages.
A major goal in CS312 is to teach you how to program well. Just about anyone can learn how to program, but it takes a deep understanding of the principles of computer science to write truly elegant and efficient programs.
We use the Standard ML (SML) programming language throughout the course. SML is a modern functional programming language with an advanced type and module system. The course is not about programming in SML. Rather, SML provides a convenient framework in which we can achieve the objectives of the course. Like the object-oriented model of Java, the functional paradigm of SML is an important programming model with which all students should be familiar, as it underlies the core of almost any high-level programming language. In addition the SML type and module systems provide frameworks for ensuring code is modular, correct, re-usable, and elegant. Other languages, such as Java, also provide facilities to achieve these goals, but the mechanisms of SML are largely orthogonal to those of object-oriented languages. By studying alternatives, students will be better equipped to use, implement or even design future programming environments that combine the best features of both worlds.
Another important reason we use SML is that it has a relatively clean and simple model that makes it easier to reason about the correctness of programs. Indeed, SML was one of the first major programming languages to have a formal semantic definition. In our studies, we will reason not only about the functional correctness of code, but also the space, time, and other resources used in a computation. The relatively simple evaluation model for SML makes it easy to do this.
The best way to reach the course staff is by posting questions or comments to the CS312 newsgroup cornell.class.cs312. You can also reach the course staff by sending email to cs312-l@lists.cs.cornell.edu. We will try to respond to questions within one working day. If we judge that the question might have been better directed to the newsgroup, the question may be forwarded there unless an explicit request is included to the contrary.
Please read these guidelines before emailing or using the newsgroups.
| Name | Position | Phone | Office/consulting hours | |
| Andrew Myers | Professor | andru@cs.cornell.edu | 255-8597 | Upson 4119C, Wed. 1:30-2:30pm |
| Maya Haridasan | TA | maya@cs.cornell.edu | 255-2219 | Upson 4162, Mon. 4:30-6:30pm |
| Jeff Vaughan | TA | jav28@cornell.edu | Syslab* UP331, Thurs. 7:30-9:30pm | |
| Tim Bavaro | TA | tmb28@cornell.edu | Syslab* UP331, Wed. 3:30-5:30pm | |
| Haakon Larsen | TA | hl272@cornell.edu | Syslab* UP331, Fri. 1:30-2:30pm | |
| Alex Fierro | Teaching Consultant | aaf25@cornell.edu | Wed Consulting | |
| Ted Tang | Teaching Consultant | tvt3@cornell.edu | Sun and Mon. Consulting | |
| Sergey Grankin | Consultant | sg252@cornell.edu | Sun. and Tues. Consulting | |
| Harlan Crystal | Consultant | hpc4@cornell.edu | ||
| Kevin Markman | Consultant | km266@cornell.edu | Tues. and Wed. Consulting | |
| Alex Cheng | Consultant | ac327@cornell.edu | Mon. Consulting |
Lectures: Tuesday and Thursday, 10:10-11:00, 219 Phillips Hall. Attendance is required.
Recitations: Monday and Wednesday. Attendance is required; students will be responsible for the new material presented in recitation. Students may attend sections other than the one they are assigned to, but be aware that different sections may cover material in slightly different order.
| 10:10-11:00 | Upson 111 | Jeff, Haakon |
| 2:30-3:20 | Hollister 306 | Tim, Alex F. |
| 3:35-4:25 | Hollister 306 | Maya, Saikat |
| 7-9pm | Upson 215 | Maya, Ted |
The TAs have regular office hours during the day, consultants have evening consulting hours. Office hours are given in the above table. Consulting hours are 7-10pm Sunday through Wednesday in Upson 304A, during heavy weeks (i.e. the week when problem set is due) and only Monday and Wednesday otherwise. The night before every project is due, we will hold extended consulting hours from 7pm-12 midnight. Consulting hours will not be held the day after the problem set is due.
There is no official textbook for the course. The following titles are excellent references and are on reserve in the Engineering library:
Two convenient online sources that we will be using from time to time are:
In addition, there are many other resources on the Web for Standard ML, including tutorials, free compilers, libraries, etc.
We will be using the Standard ML of New Jersey (SML/NJ version 110) compiler, interactive system, libraries, and tools for all examples and homework. SML/NJ is a freely available, open source development system brought to you from Lucent's Bell Labs -- the same place that developed C and Unix. SML/NJ runs under Win32 systems and just about any flavor of Unix. Sadly, there is no support for the Macintosh. There are other SML compilers freely available, such as MoscowML which do run on the Macintosh. However, there are subtle differences between systems, so we recommend that you use SML/NJ if possible. In particular, we will be using SML/NJ's make system (called the Compilation Manager or CM for short) which provides a convenient way to organize programming projects and to build large systems.
Students are responsible for all material in the assigned readings, as well as material covered in lectures and in recitations. There will be six problem sets, two preliminary exams, and a final exam. Each problem set will involve a programming assignment and may include written exercises. Exams will cover material presented in class and will require you to do some heavy thinking on your feet. All will contribute to your final score, roughly as follows:
| Problem Sets | Subject | % of score |
| Problem Set 1 | Introduction to ML | 3% |
| Problem Set 2 | More ML programming | 6% |
| Problem Set 3 | Designing and implementing ADTs | 8% |
| Problem Set 4 | Modular design | 10% |
| Problem Set 5 | Concurrent Interpreter | 10% |
| Problem Set 6 | Robot Game (Lambda-Shark) | 13% |
| Exams | Date | % of score |
| Prelim I | March 11 | 10% |
| Prelim II | April 20 | 10% |
| Final Exam | May 14 | 30% |
No late assignments will be accepted, but we generally grade assignments the same night they are due and return them immediately. Programs are submitted online. They are due at 11:59pm on the due date; the submission system will be disabled at that time. You should try to get started on the programming assignments early. The best use of your time and the machine's time is to think about the problems before typing anything at the computer. (No matter how many times we say this, it takes a long time to sink in: think before typing.)
Makeup exams must be scheduled within two weeks of the start of class. Check now to see if you have a conflict with another class and contact Professor Myers immediately to reschedule.
Problem sets 1–3 must be done individually. On problem sets 4–6, you will work in pairs. You will submit a single joint assignment with both names on it.
Under no circumstances may you hand in work done with (or by) someone else under your own name. If you have a question, consult the course staff. Your code should never be shared with anyone other than your partner. You would be amazed at how easy it is to tell when people have worked together on problem sets, so please don't make life unpleasant for all of us by breaking these rules. The penalties for cheating at Cornell are severe, and include expulsion; see the CS Department's Code of Academic Integrity. If you are unsure about anything, please ask.
CIT and various colleges on campus provide public Macintosh and PC facilities. We have only installed SML/NJ on the Windows machines. You may use your own machine or the public ones. If you are interested in installing SML/NJ or MoscowML on your own machine, then see the course software page for details. The CS department does not provide computer facilities for this course. This means that students from other courses have priority in the CSUG lab.