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This is the course syllabus for Nic McPhee's Fall 2019 Problem solving and algorithm development (CSci 1301) course at the University of Minnesota, Morris. For additional information (e.g., events and assignments) see the course Canvas site.
This is a 4 credit course which meets MWF 10:30-11:35am, in Sci 2200. There are no pre-requisites for this course and we will not assume any prior experience with computer programming.
- Course description
- Course goals and learning objectives
- Nic's Fall, 2019, office hours
- Nic's contact info
- Texts and other materials
- Course outline
- Course work and grading
- General policies and information
The course description from the catalog:
Introduction to different problem solving approaches, major programming paradigms, hardware, software, and data representation. Study of the functional programming paradigm, concentrating on recursion and inductively-defined data structures. Simple searching and sorting algorithms.
We're all used to what we might call informal problem solving. A problem presents itself, and we work out a rough idea of a solution that's sufficient for our understanding; we know what we mean, and we don't bother to work out the details in advance. If we're working on a team we sometimes need to provide a little more detail to ensure that all the team members are on the same page, but there's typically still an awful lot that we don't spell out, as we (often implicitly) assume that the shared experiences of the team will allow people to fill in the blanks.
When working with computers, however, we're no longer able to skip over "little" details and assume the computer will "fill in the blanks". Computers have never walked, eaten cereal, or knit a sock. They've not read Harry Potter or watched Lawrence of Arabia. People and computers have effectively no shared experience, and consequently you have to spell out pretty much every step in the solution to a problem.
This course will provide an introduction to the concept of algorithms, which are essentially just a type of formalized solution that's still human readable, but much closer to something that could be run on a computer. Algorithm development, however, turns out to be a quite powerful problem solving tool in a variety of contexts, not just in computer programming; ancient Babylonians developed a host of practical, important algorithms, and Euclid's algorithm for computing the greatest common divisor of a pair of numbers is still widely used today even though he developed it thousands of years before the invention of electronic computers.
Given this framework, programming is that final step, where we express an algorithm in a formal notation (known as a programming language) that can be executed by a computer. Note, however, that (as the example of Euclid suggests) the concept of algorithms is important and interesting even without computers to run them; the existence of computers, on the other hand, is key to the economic value of much of programming and the reason that companies like Google make money hand over fist.
Some of the key learning goals in this course include:
- You will learn about algorithms - what an algorithm is, some classic algorithms, etc.
- You will learn about the importance of formal syntax in computing and how to recognize and generate legal text in one or more formal syntaxes.
- You will learn some useful problem solving techniques including defining cases, using conditional statements, and functional decomposition (divide and conquer)
- You will learn about recursion
- You will learn about scope
- You will learn about higher-order functions (procedural abstraction - passing procedures as parameters)
- You will learn not only to program, but also why we write programs, how to communicate with the computer, and some basic things about how a computer works
We should be able to cover significant ports of the course text. There is no expectation in this course that you've had any prior programming experience. We'll do most of the programming in this class using the Racket programming language.
My office hours for Fall, 2015, are:
- Monday, 3:30-5pm
- Tuesday, 1-2pm
- Friday, 9-10am
I'll typically start my office hours in my office (Sci 1315), but I may well move to the Computer Science labs (Sci 2610, 2630, and 2650) if, for example, a group has a question about a project they're working on. If I leave the office during office hours I'll try to leave a note on my door telling you where I am, but you should always feel free to check the labs if you're looking for me.
Those times almost certainly won't work for everyone. You're always welcome to just drop by (see my complete schedule), and I'm happy to make appointments anytime I'm free.
If these times work very badly for you (e.g., you have class conflicts all of these hours) then please let me know. If it turns out that a lot of folks have major conflicts then I'll want to reconsider my choices.
Email (mcphee@…) is my preferred method of contact, but be warned that I get a lot of e-mail and bits get dropped now and then. Phone (office: 320-589-6320) or a text is often better if it's critical or you need immediate feedback.
My office is Sci 1315, downstairs at the east end of the Science office wing.
I use the U's Google calendar to manage my calendar. Google may be the Evil Empire of the next decade, but they make a fine on-line calendar tool. If you're using Google calendar, feel free to use that to invite me to events/appointments.
This course has 1 required text:
- How to design programs, SECOND EDITION (often abbreviated HtDP2e) by Felleisen, Findler, Flatt, and Krishnamurthi
- This is available freely on-line, and this edition is only available on-line: https://htdp.org/2019-02-24/
⚠️ Don't buy a printed copy of the first edition unless you really want to. The second edition is very different, and the course really won't make a lot of sense is you've got an old copy of the book.
There will be various other on-line materials, mostly shared via Canvas or on the resources page.
Since this course is ultimately about problem solving, having other sources of problems to play with can be helpful. A few that support Racket (the language we'll be focusing on here) include:
- CodeWars is, despite the unfortunately competitive name, an excellent place to practice your programming and problem solving skills. They support a lot of different programming languages, including Racket.
- Project Euler is an excellent, if perhaps a bit math-y, set of practice problems that only checks your answer and so doesn't care what language you use.
- Python Challenge is quite cool and, despite the name, doesn't require the use of Python or any other specific language. That said, it tends to be pretty heavy on text processing, which is something Racket can do just fine, but isn't something our text emphasizes.
- Google Code Jam is an excellent set of programming-contest-style problems, and they're language agnostic.
See the course Canvas site for the calendar and outline of the course.
The components of the course grade are:
- Homework and labs: 35%
- 2 Mid-terms: 20% each
- Final: 20%
- Quizzes: 5%
There will be a combination of labs and problem sets throughout the semester, roughly one per week. The there will be a handful of short quizzes; these will not be announced in advanced. The lowest quiz grade will be dropped (i.e. not counted towards the quiz total). A missed quiz will receive a grade of zero and thus will be counted as the lowest grade, unless it was missed due to an illness or other circumstances beyond your control. If you missed a quiz or a lab because of an illness or similar circumstances, it is your responsibility to communicate these reasons to me as soon as possible and arrange for a make-up work.
There will be two exams (which may be a combination of in-class and take-home), and a final exam (which may also combine in-class and take-home). The in-class part of the first exam will be on Monday, 30 September. The in-class part of the second exam will be on Monday, 28 October. The in-class finals period is scheduled for Friday, 19 December, from 8:30-10:30am.
Key elements for any work in this course (and arguably any work in most any area of computing) include:
- Correctness
- Readability
- Clarity
- Organization
Many of the assignments and test questions will come with a set of instructor (or TA) generated test cases that you can use as a self-assessment tool before you turn in your work. If your programs don't pass our tests, then you should assume something's broken and seek assistance. Note, however, that simply passing all of the given tests is no guarantee of full marks. It's possible, e.g., that the given tests don't cover all possible cases (either accidentally or deliberately). Also readability, clarity, and organization are all key properties we want all programs to have since you're communicating with other people as much as you're communicating with the computer, and none of these properties can easily be assessed via tests.
Grades will be entered on the course Canvas site and can be checked there throughout the semester. If you have any questions or concerns, please bring them up right away when it's much easier to address them.
Late work will receive a penalty of 10% for every day that it is late. This can be overwritten in case of illness and other unavoidable delays, but you'll need to contact me promptly to address such cases.
According to University of Minnesota policy:
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A: Represents achievement that significantly exceeds expectations in the course.
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B: Represents achievement that is above the minimum expectations in the course.
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C: Represents achievement that meets the minimum expectations in the course.
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D: Represents achievement that partially meets the minimum expectations in the course. Credit is earned but it may not fulfill major or program requirements.
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S: Satisfactory (equivalent to a C- or better)
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F: Represents failure in the course and no credit is earned.
By default I will use the University Minnesota Letter Grade Scheme:
- A: 100% to 93%
- A-: <93% to 90%
- B+: <90% to 87%
- B: <87% to 83%
- B-: <83% to 80%
- C+: <80% to 77%
- C: <77% to 73%
- C-: <73% to 70%
- D+: <70% to 67%
- D: <67% to 60%
- F: <60% to 0%
I will never be less generous than this, but I may be more generous depending on the clustering and gaps in the grades. If, for example, there's a dense cluster from 88.5% to 86% followed by a gap, I might extend the B+ range down to 86%. I make no promises that I will make such shifts, but it could happen.
For undergraduate courses, one credit is defined as equivalent to an average of three hours of learning effort per week (over a full semester) necessary for an average student to achieve an average grade (a C) in the course. For example, an average student taking a four credit course that meets for four hours a week should expect to spend an additional eight hours a week on course work outside the classroom in order to get a C in that course.
The University of Minnesota views disability as an important aspect of diversity, and is committed to providing equitable access to learning opportunities for all students. The Disability Resource Center (DRC) is the campus office that collaborates with students who have disabilities to provide and/or arrange reasonable accommodations.
- If you have, or think you have, a disability in any area such as, mental health, attention, learning, chronic health, sensory, or physical, please contact the DRC office on your campus (UM Morris 320.589.6178) to arrange a confidential discussion regarding equitable access and reasonable accommodations.
- Students with short-term disabilities, such as a broken arm, should be able to work with instructors to remove classroom barriers. In situations where additional assistance is needed, students should contact the DRC as noted above.
- If you are registered with the DRC and have a disability accommodation letter dated for this semester or this year, please contact your instructor early in the semester to review how the accommodations will be applied in the course.
- If you are registered with the DRC and have questions or concerns about your accommodations please contact the Coordinator of the Disability Resource Center.
Additional information and contact information is available on the DRC website: https://academics.morris.umn.edu/office-academic-success/disability-resource-center, or you can go visit then in the Library.
University policy prohibits sexual harassment as defined by the University of Minnesota Regents' policy. In general, harassment or intimidation of others in the class for whatever reason is unacceptable (and hardly conducive to a successful learning environment).
The University provides equal access to and opportunity in its programs and facilities, without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. For more information, please consult Board of Regents Policy.
Academic integrity is essential to a positive teaching and learning environment. All students enrolled in University courses are expected to complete coursework responsibilities with fairness and honesty. Failure to do so by seeking unfair advantage over others or misrepresenting someone else's work as your own, can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as follows:
Scholastic dishonesty means plagiarism; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering, forging, misrepresenting, or misusing a University academic record; or fabricating or falsifying data, research procedures, or data analysis.
Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and including an "F" or "N" for the course. If you have any questions regarding the expectations for a specific assignment or exam, ask.