Class 2: Getting Started with Linux

Held: Tuesday, 24 January 2017

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).

Preliminaries

Related Pages

• Eboard 2 (Source)

• Reading: Parts of Algorithms

• Reading: Grinnell’s Linux Environment

• Lab: Getting Started with Linux

Updates

News / Etc.

• Signed academic honesty policy due!
  • I’m using it as today’s attendance.
  • If you are uncomfortable signing it, make an appointment to chat with me.
• You should have received one handout at the start of class.
  • Lab: Getting Started with Linux

Upcoming Work

• Assignment 1. Due Tonight!
• Reading: Beginning Scheme Due Wednesday
• Reading: The DrRacket Programming Environment Due Wednesday
• Reading: How Scheme Evaluates Expressions Due Wednesday

Extra credit (Academic)

• Rosenfield MLK day talk Wednesday evening at 7pm, JRC 101
• Thursday extras, Thursday, 4:15 p.m., Science 3821: Summer Research Opportunities in CS

Extra credit (Peer)

• Open practice for crazy improv group 2-4 Saturday “Ritalin Test Squad”
• Swim meet next weekend.

Good things to do

• Rest.

Structure of Today’s Class

• Administrivia [5 min]
• Small group: Making groups [5 min]
• Small group: What did you learn yesterday? [5 min]
  • Yes, we all got “be precise!” What else?
  • Introduce yourselves!
  • Five or so learning outcomes from yesterday.
• Large group: Share answers [5 min]
  • I will call on folks randomly
• Small group: What did you learn about algorithms? [5 min]
  • Think about what I might ask.
  • Come up with questions for me.
• Large group: Overview of algorithms [5 min]
• Lab [20 min]
  • Leave when you are done.

Alternate Structure of Today’s Class

• Sam: Additional class issues. [5 min]
• Small group: Discuss the reading on algorithms. [5 min]
  • Don’t forget to introduce yourselves!
  • Make sure that you are ready to answer questions I might ask about the parts of an algorithm.
  • Develop a list of questions for me.
• Large group: Overview of Algorithms. [5 min]
• Small group: Example of each component from AB&J discussion [5 min]
• Large group: Review examples. [5 min]
• Lab. [20 min]
• Leave.

About This Course

• 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, particularly in that we ask you to do reading and reflection before each class.)
  • 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.
  • That is, you can solve problems in many different domains. We are going to use image making as our problem domain.
  • We’ve found that images are useful for a wide variety of reasons, not least of which is that it’s sometimes much easier to “see” where things went wrong.

Reflections on Making Sandwiches

What else did you learn from the No-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.

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.

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.

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”.

Debriefing, Revisited

• Where did you see each aspect of an algorithm in the sandwich making exercise?

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 Tuesdays.
• 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.