CS 15-212: Principles of Programming (Spring 2009)

About this course

Description

This course has the purpose of introducing students who have had experience with basic data structures and algorithms to more advanced skills, concepts and techniques in programming and Computer Science in general. This will be accomplished along three dimensions.

• The main skill you will get out of this course is a logical attitude toward problem solving. You will learn how to decompose a problem into manageable parts, how to compose their solutions into a complete program, and how to reason about programs to ensure that they are correct
• As we do that, you will be exposed to some advanced but pervasive concepts in Computer Science and programming. In particular, you will learn about induction and recursion, program correctness, symbolic computation, search, grammars and parsing, structuring large programs, computability, continuations, streams and more
• Our vehicle for achieving these objectives will be Standard ML, one of the most advanced experimental programming languages brewing in the labs worldwide. It will expose you to a number of high-level programming techniques such as recursive functions, higher-order functions, data abstraction, polymorphism, exceptions and modularity. Bits and pieces are found in current commercial programming language such as Java, and more will be part of the programming languages of the future

Prerequisites

Software

The course relies extensively on the programming language Standard ML (SML) and related utilities, mainly ML-Lex, ML-Yacc and Concurrent ML. The particular implementation we will be working with is Standard ML of New Jersey (SML/NJ), version 110.65.

SML at CMU-Q

A reference build has been made available in the Unix clusters. To run it, you need to login into your Unix account. In Windows, you do this by firing PuTTy and specifying unix.qatar.cmu.edu as the machine name. When the PuTTy window comes up, type sml, do your work, and then hit CTRL-D when you are done.

You can edit your files directly under Unix (the easiest way is to run the X-Win32 utility from Windows and then run the Emacs editor from the PuTTy window by typing emacs - see also this tutorial).

If you want to do all this from your own laptop, you first need to install X-Win32 from here. PuTTy is pre-installed in Windows.

SML on Your Own Laptop

If you want, you can install a personal copy of SML/NJ on your laptop. To do this, download this file and follow these instructions Personal copies are for your convenience: all software will be evaluated on the reference environment on unix.qatar.cmu.edu. You need to make sure that your homework assignments work there before submitting them. To do so, you need to transfer your files onto unix.qatar.cmu.edu and test them there. You can do so by using the PSFTP utility which comes with PuTTy (or any of the many more user-friendly FTP front-ends).

Documentation

Useful documentation can be found on the SML/NJ web site. The following files will be particularly useful:

• Using the SML/NJ System, by Peter Lee, answers the following question: Ok, I've got the SML prompt, now what do I do?
• The SML Basis Manual Pages describes the functionalities made available in the SML basis library
• The ML-Lex user's manual, by Andrew Appel, James Mattson and David Tarditi
• The ML-Yacc user's manual, by David Tarditi and Andrew Appel
• The Concurrent ML Reference Manual

Readings

The 15-212 Wiki

The material for all lectures can be found on the 15-212 wiki. This is wiki, not a textbook. The main differences are:

• It is dynamic: the content will change over time (for the better)
• It will always be work in progress
• Everybody can make updates. This means YOU!

Further References

• Michael R. Hansen and Hans Rischel, Introduction to Programming Using SML, Addison-Wesley, 1999.
• Robert Harper, Programming in Standard ML, working draft of April 23, 2007   (version for browsing,   version for printing)
• Lawrence C. Paulson, ML for the Working Programmer, 2nd edition, Cambridge University Press, 1996.
  Note: This book refers to an older specification of SML and programs in it may not work in current SML environments.

Grading

• Participation: 15%
  This has three components:
  • Sunday quizzes on the previous week's lectures
  • Class participation: volunteer to answer questions asked to the class
  • Class preparedness: you must have done the readings before coming to class
  • Involvment with the wiki: if you see an error, correct it; if you have a better explanation for something, change it; if you see something missing, add it

	Hw1 5%	Hw2 5%	Hw3 5%	Hw4 10%	Midterm 10%	Hw5 10%	Hw6 10%	Hw7 5%	Hw8 10%	Final 15%
Posted	11 Jan	17 Jan	24 Jan	31 Jan	24 Feb	14 Feb	07 Mar	21 Mar	04 Apr	27 Apr (2pm) 1190
Due (23:59)	17 Jan	24 Jan	31 Jan	14 Feb		07 Mar	21 Mar	04 Apr	18 Apr
Corrected	24 Jan	31 Jan	07 Feb	21 Feb	03 Mar	14 Mar	04 Apr	11 Apr	25 Apr	04 May

• Homework assignments: 60%
  • 6 to 12 assignments
  • 5% for some and 10% for others, for a total of 60%
  • Written and programming parts. A program is like an essay!
  • Handed out on Saturdays
  • Due on Saturdays at 11:59pm Doha time. Submit on the Blackboard dropbox.
    • You have a total of 3 late days
    • After that, late homeworks will be accepted until the beginning of next recitation, but with a 25% penalty
  • Graded by the following Sunday. Evaluated on the basis of
    • Correctness: your program should work in the reference environment
    • Specification: structured comments describing types, meaning of the returned value, invariants, and side-effects
    • Elegance: See these notes about style
  • To encourage good work and integrity, the instructor may invite students to his office to explain their solutions. Should this happen, the students' explanations will become part of their grades for that assignment.
  • No joint assignments unless explicitly instructed
• Midterm exam: 10%, in class, closed books
• Final exam: 15%, 3 hours, open books and notes (but not laptops)
• Bonus points: up to 10% for particularly elegant solutions
• Negative points: up to 100% if caught cheating
• Don't cheat!

Assessment

Course Objectives

This course seeks to develop students who:

1. can leverage the mathematical structure of a problem to develop a solution
2. can use abstraction and modularity to manage complexity
3. can use formal arguments to prove the correctness of a problem solution
4. master a non-declarative programming paradigm
5. have gained advanced skills, concepts and techniques in programming and Computer Science

Learning Outcomes

Upon successful completion of this course, students will be able to:

1. explain and use basic programming language concepts such as typing, evaluation, declarations, expressions, values, and types
2. explain and use advanced programming language concepts such as data types, pattern matching, polymorphism, higher-order functions, continuations, exceptions, streams, memoization, modularity, formal language hierarchy
3. design recursive algorithms and develop recursive programs
4. use mathematical induction to prove program correctness
5. model problems in Computer Science using lists, trees and graphs
6. program symbolic solutions to problems using data types and pattern matching
7. use polymorphism and functional arguments to build reusable program modules
8. develop abstract and parametric modules for code reusability
9. write a grammar for a language and program a basic parser
10. recognize non-computable problems and give formal arguments to support non-computability claims