Difference between revisions of "CISC220 F2023"

Comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation. Topics include recursion, stacks, queues, lists, heaps, hash tables, search trees, sorting, and graphs.

Full: http://nameless.cis.udel.edu/class_wiki/index.php/CISC220_F2023

??, E-mail: ??, office hours: ?? in Smith 102A (??)

• Lectures: Tuesdays and Thursdays 11:10 am to 12:30 pm in GORE 219
• Labs: Thursdays 2:20 to 3:15 pm in CLB 046 and Fridays 3:00 to 3:55 pm in ISE 202. In the schedule below note that there is NOT a lab every week

• 50% Labs (5% each). These are problem sets/smaller programming exercises which are assigned in lab most weeks and due by midnight the night before the next lab. All written answers must be in PDF form. Attendance at labs is expected--this is your chance to ask questions face to face and get started early on the assignment
• 10% Open-ended programming project. Subject to a few constraints, you will be free to implement and apply a data structure and/or algorithm of your choosing.
• 20% Midterm
• 20% Final (essentially a midterm for the second half of the course)

Your labs and programming projects are due by midnight of the deadline day (with a small grace period afterward). All should be submitted directly to Canvas--e-mail submissions will not be accepted. A late homework is a 0 without a valid prior excuse. To give you a little flexibility, you have 6 "late days" to use over the semester to extend the deadline by one day each without penalty. No more than two late days may be used per assignment. Late days will automatically be subtracted, but as a courtesy please notify the instructor and TA in an e-mail of your intention to use them before the deadline.

For the overall course grade, a preliminary absolute mark will be assigned to each student based on the percentage of the total possible points they earn according to the standard formula: A = 90-100, B = 80-90, C = 70-80, etc., with +'s and -'s given for the upper and lower third of each range, respectively. Based on the distribution of preliminary grades for all students (i.e., "the curve"), the instructor may increase these grades monotonically to calculate final grades. This means that your final grade can't be lower than your preliminary grade, and your final grade won't be higher than that of anyone who had a higher preliminary grade.

I will try to keep you informed about your standing throughout the semester. If you have any questions about grading or expectations at any time, please feel free to ask me.

Data Structures and Algorithms in C++ (4th ed.), Adam Drozdek. It is NOT at the textbook store (at least not new). Suggested sources:

• Amazon $10+ used, $33 paperback
• Google Play e-book $40 rental, $70 purchase

Code examples from the book can be downloaded here

On certain assignments where the instructions explicitly grant permission, students may use generative AI tools such as OpenAI's ChatGPT, GitHub's Copilot, Meta's Code Llama, etc. Where no such permission is granted or nothing is said, the default assumption is for you to do everything with your own human brain. If and when you use an AI tool (with permission), it MUST be acknowledged with a citation along the lines of these guidelines (i.e., specific tool, date, prompt or prompts used, as well as any other useful context). Such citations should be added as comments to any code files which contain AI-generated code, and a README file with all such citations should be included with any homework submission. AI or search tool usage during any in-class quizzes is prohibited.

cplusplus.com tutorial: Basics, Program Structure, Compound Data Types

cplusplus.com tutorial: Classes I & II, Special Members

cplusplus.com tutorial: Classes II, STL reference

Register/add deadline

Post-C++ lecture notes

Wikipedia entry, UW slides (first 5 pages of PDF)
Optional: Princeton slides

Optional: Path-finding tutorial (stop at "Heuristic search")

Optional: Sorting algorithms animated