Held: Tuesday, 21 January 2014

Back to Outline 01 - An Introduction to Algorithms. On to Outline 03 - An Introduction to Scheme.

Summary

Today 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).

Related Pages

• Reading: The Linux Environment
• Reading: Parts of Algorithms
• Lab: Getting Started with Linux
• EBoard (Source) (HTML) (PDF)

Overview

• Lessons from day one.
• Common parts of an algorithm.
• About the course.
• Getting started with Linux.
• Some administrative details (if there's time).

Administrivia

• Yes, I'll take attendance again.
• Don't forget that the first assignment is due at 10:30 p.m. tonight. I appreciate that assignments have started arriving.
• There are lots of readings for tomorrow's class, but they are all short.
• Extra credit: MLK Day Talk, tonight @ 7:30 in JRC 101.
• Extra credit: Thursday extra on summer research in CS, Thursday @ 4:30 in Noyce 3821.
• Extra credit: CS Table Friday at noon in the Day PDR in the Marketplace.

Reflections on Making Sandwiches

What else did you learn from the Nut Butter and Jelly exercise?

The Components of an Algorithm

• As you may have noted, there are some common aspects to algorithms. That is, there are techniques that we use in many of the algorithms we write.
• It is worthwhile to think about these algorithm parts because we can rely on them when we write new algorithms.

Variables: Named Values

• As we write algorithms, we like to name things.
• Sometimes we use long names, such as "the piece of bread in your dominant hand".
• Sometimes, we use shorter names, such as "bread-dom".
• As we start to write more formal algorithms, we will need techniques for noting which names we are using and indicating what they name (and, sometimes, what kind of thing they name).
• We call these named values variables, even though they don't always vary.
• We need to be careful to use unambiguous names. Recall the problems with using "it" in your descriptions.

Parameters: Named Inputs

• Many algorithms take data as input (and generate other data as output).
• The "open a jar" algorithm takes the jar as an input.
• A "find square root" algorithm would take a number as input.
• A "look up a telephone number" algorithm might take a phone book and a name to lok for as inputs.
• In each case, the algorithm should works on many different inputs.
• The algorithm works as long as the input is "reasonable" (we can't find the square root of a piece of bread, we can't make a sandwich with pi).
• We call these inputs parameters.

Conditionals: Handling Different Situations

• At times, our algorithms have to account for different conditions, doing different things depending on those conditions.
• In our PB and J algorithm, we had to consider the type of lid for the jar.
• We call such operations conditionals.
• Conditionals typically take either the form
  if some condition holds then do something
• Here's a slightly more complex form
  if some condition holds then do something otherwise do something else
• At times, we need to decide between more than two possibilities. Typically, we organize those as a sequence of tests (called guards) and corresponding things to do.

Repetition

• At times, our algorithms require us to do something again and again.
• For example, you told me to repeatedly twist the jar top.
• We call this technique "repetition".
• Again, repetition takes many forms.
• We might do work until we've reached a desired state.
• We might continue work as long as we're in some state.
• We might repeat an action a fixed number of times.
• You can probably think of many other forms of repetition.
• As in the jar top example, you need to be careful to ensure that your repetition terminates.

Subroutines: Named Helper Algorithms

• Many algorithms require common actions for their operation.
• Most of you wrote a common "How to open a jar" action.
• If we were going to make lots of sandwiches, your instructions would become a common action.
• We can write additional algorithms for these common actions and use them as part of our broader algorithm.
• We can also use them in other algorithms.
• We call these helper algorithms "subroutines".

About This Class

• Computer Science 151 has a number of goals
  • To introduce you to fundamental ideas of computer science: abstraction, algorithms, and data
  • To enhance your problem-solving skills and give you experience in formal representation of problems and solutions.
  • To introduce you to two primary paradigms of problem solving: functional and imperative.
  • To give you some programming skills that you can apply to problems in other disciplines.
  • To improve other learning and thinking skills (as we hope all Grinnell classes will do).
• I expect and hope that you will find CSC151 different from any class you've taken in the past.
  • We use a different format than many classes: a collaborative, workshop-style format. (You may have seen this format in other introductory science courses; we do it somewhat differently.)
  • Computers and computer science also require you to think differently. I expect that you'll exercise some brain cells you may have forgotten you have. (And after all, isn't liberal arts education an exercise in thinking in as many ways as you can?)
• Like most computer science courses, CSC151 will have both theoretical and practical components. I hope you will enjoy relating the two.
• And, hey, we're going to make pretty pictures, too.

Lab: Getting Started with Linux

• We'll break about midway through today's class to get you set up working with our Linux computers.
• This "lab prep" is somewhat pointless and annoying, but also necessary.
• You should not work in a team for today's lab, and we'll spread out to both this room and the neighboring room.
• Do the lab

Administrative Issues

I am unlikely to cover these issues in class today. I include them in the outline mostly for your edification.

• Please refer to the course web site for more details.
• Teaching philosophy: I support your learning.
• Policies
  • Attendance: I expect you to attend every class. I understand that sometimes you have to miss. Let me know when and wy.
  • Grading: I'm a hard grader. I don't grade everything.
  • Course web.
  • Etc.
• Daily work
  • Attend class, work on lab and participate in discussion.
  • Finish the lab in the evening.
  • Do the reading for the next class in the evening.
• The quizzes
  • Every Friday, plus a few pop quizzes
• The exams
  • Three take-home exams during the semester. Plan to spend four to five hours on each one.
  • An optional final to make up for a bad exam grade.
  • Take all three exams anyway.
• The labs
  • Available online.
• The homework
  • Weekly, due on Wednesdays
• Academic honesty
  • Core to the academic process.
  • The college's basic policy: Cite carefully.
  • My basic policy: Don't cheat. (Also: It's usually okay to work with others, provided you cite them.)
  • Read my handout on academic honesty carefully.