Difference between revisions of "CISC220 F2023"
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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 [http://www.udel.edu/canvas 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. | 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 [http://www.udel.edu/canvas 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. | ||
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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. | 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. | ||
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| − | |valign="top"|'''AI policy''' | + | |valign="top"|'''Collaboration and AI policy''' |
| − | | | + | |Students can discuss problems with one another in general terms, but must work independently on all assignments ''except when pairs or teams are permitted''. This also applies to online and printed resources: you may consult them as references (as long as you cite them), but the words you turn in must be yours alone. Any quoting must be clear and appropriately cited. The University's policies on academic dishonesty are set forth in the student code of conduct [https://www.udel.edu/students/community-standards/student-guide/ here]. |
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| + | Students are allowed to used generative AI tools such as [https://openai.com/chatgpt OpenAI's ChatGPT], [https://github.com/features/copilot GitHub's Copilot], [https://github.com/facebookresearch/codellama Meta's Code Llama], etc. on SOME assignments in this course. Lab, homework, and project instructions will explicitly state "AI permitted" or "AI not permitted". When such a tool is used, it MUST be acknowledged with a citation along the lines of [https://subjectguides.uwaterloo.ca/chatgpt_generative_ai/aigeneratedcontentcitation these guidelines] (i.e., specific tool, date, prompt or prompts used, as well as any other useful context). AI or search tool usage during any in-class quizzes is prohibited. | ||
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Revision as of 10:26, 28 August 2023
Course information
| Description | CISC 220 -- Data Structures (Honors) 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. |
| Requirements | This is a course for undergraduates who have obstained a grade of C- or better in CISC 181, and have taken or are currently taking CISC 210 and MATH 241. |
| Instructor | Christopher Rasmussen E-mail: cer@cis.udel.edu Office: Smith 446 Office hours: Wednesdays, 2:30-4:30 pm |
| URL |
Full: http://nameless.cis.udel.edu/class_wiki/index.php/CISC220_F2023 |
| TA |
??, E-mail: ??, office hours: ?? in Smith 102A (??) |
| Schedule | |
| Grading |
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. |
| Textbook |
Data Structures and Algorithms in C++ (4th ed.), Adam Drozdek. It is NOT at the textbook store (at least not new). Suggested sources:
Code examples from the book can be downloaded here |
| Collaboration and AI policy | Students can discuss problems with one another in general terms, but must work independently on all assignments except when pairs or teams are permitted. This also applies to online and printed resources: you may consult them as references (as long as you cite them), but the words you turn in must be yours alone. Any quoting must be clear and appropriately cited. The University's policies on academic dishonesty are set forth in the student code of conduct here.
Students are allowed to used generative AI tools such as OpenAI's ChatGPT, GitHub's Copilot, Meta's Code Llama, etc. on SOME assignments in this course. Lab, homework, and project instructions will explicitly state "AI permitted" or "AI not permitted". When such a tool is used, 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). AI or search tool usage during any in-class quizzes is prohibited. |
Schedule
Note: The blue squares in the "#" column below indicate Tuesdays. Tan rows are lab days (Thursdays/Fridays). All lectures (except YouTube posts) should be available on UDCapture
2023-2024 UD academic calendar
| # | Date | Topic | Notes | Readings | Links |
|---|---|---|---|---|---|
| 1 | Aug. 29 | Introduction | Big four topics on data structures and algorithms: abstraction, implementation, analysis, and applications | Drozdek 1.1-1.3 |
|
| 2 | Aug. 31 | C++ review | C++ basics: differences with C, arrays, I/O, random numbers, new/delete, static vs. dynamic memory allocation | C vs. C++ C++ for Java programmers cheat sheets: [1], [2] cplusplus.com tutorial: Basics, Program Structure, Compound Data Types |
|
| Aug. 31/Sep. 1 | LAB #1 | ||||
| 3 | Sep. 5 | C++ review | ADTs, classes, destructors, constructors, assignments | Drozdek 1.4 (skip 1.4.5) |
cplusplus_2a.tar |
| 4 | Sep. 7 | C++ review | Function & class templates, STL | Drozdek 1.7-1.8 |
template_test, anythingcell |
| Sep. 7/Sep. 8 | LAB #2 | ||||
| 5 | Sep. 12 Register/add deadline |
Stacks | ADT (including STL) and applications, including stacks for postfix expression evaluation | Drozdek 4-4.1 | |
| 6 | Sep. 14 | Stacks and queues | Implementing stacks with linear arrays; queue ADT, applications, and linear array implementation | Drozdek 4.1, 4.2 | array_stack, array_queue |
| Sep.14/Sep. 15 | LAB #3 | ||||
| 7 | Sep. 19 | Queues, deques, and lists | Circular arrays for queues, singly- and doubly-linked lists for stacks and queues | Drozdek 3-3.2, 3.7, 3.8, 4.2, 4.4, 4.5 | sll_stack |
| 8 | Sep. 21 | Trees | Terminology; representation in general case; pre- and post-order traversals; binary trees | Drozdek 6-6.2, 6.4-6.4.2 | |
| Sep.21/Sep. 22 | LAB #4 | ||||
| 9 | Sep. 26 | Trees | Binary trees for arithmetic expressions; in-order traversals; binary search trees | Drozdek 6.3, 6.5-6.6 (skip 6.6.1), 6.12 (expression trees) | |
| Sep. 28 | |||||
| Sep.28/Sep. 29 | LAB #5 | ||||
| 10 | Oct. 3 | Algorithm analysis | Big-O notation and common complexity classes; analyzing code to obtain big-O estimates | Drozdek 2-2.3, 2.5-2.6, 2.7 | |
| 11 | Oct. 5 | Balanced binary trees | AVL trees: definition, balance notation, rotations | Drozdek 6.7-6.7.2 (skip 6.7.1) | Rotation applet |
| Oct. 5/Oct. 6 | LAB #6 | ||||
| 12 | Oct. 10 | ||||
| 13 | Oct. 12 | Balanced binary trees | AVL trees: applying rotations to restore balance property | Drozdek 6.7-6.7.2 (skip 6.7.1) | |
| Oct. 12/Oct. 13 | LAB #7 | ||||
| 14 | Oct. 17 | Midterm review | Midterm review slides |
2010 midterm (ignore questions 2 and 6) | |
| 15 | Oct. 19 | MIDTERM | |||
| Oct. 19/Oct. 20 | NO LAB THIS WEEK | ||||
| 16 | Oct. 24 |
Priority queues | ADT, heap implementation | Drozdek 4.3, 4.6, 6.9 | STL PQ example |
| 17 | Oct. 26 | Priority queues | Finish heap details | ||
| Oct. 26/Oct. 27 | NO LAB THIS WEEK | ||||
| 18 | Oct. 31 | Disjoint sets | Union-find algorithm | Drozdek 8.4.1 Wikipedia entry, UW slides (first 5 pages of PDF) |
|
| 19 | Nov. 2 | Disjoint sets | Smart union, path compression, maze generation application | ||
| Nov. 2/Nov. 3 | LAB #8 | ||||
| 20 | Nov. 7 | Compression | Huffman coding, tries | Drozdek 11-11.2 (skip 11.2.1) | |
| 21 | Nov. 9 | Finish compression; maps | Drozdek, 7.1.10 | STL map example | |
| Nov. 9/Nov. 10 | LAB #9 | ||||
| 22 | Nov. 14 Withdraw deadline Nov. 13 |
Hashing | Hash function, probing (linear, quadratic, double hashing), chaining | Drozdek 10-10.2.2 | |
| 23 | Nov. 16 | Hashing | Deletions; applications to file integrity verification, password storage | Drozdek 10.3 Illustrated Guide to Cryptographic Hashes |
Project assigned |
| Nov. 16/Nov. 17 | NO IN-PERSON LAB THIS WEEK -- BUT LAB #10 ASSIGNED | ||||
| Nov. 21 | NO LECTURE TODAY Thanksgiving |
||||
| Nov. 24 | NO LAB THIS WEEK | ||||
| Nov. 23 | NO LECTURE TODAY Thanksgiving |
||||
| 24 | Nov. 28 | Graphs | Terminology, applications, representations: adjacency matrix, adjacency lists | Drozdek 8-8.1 | slides |
| 25 | Nov. 30 | Graphs | Traversals: depth-first, breadth-first | Drozdek 8.2, 8.3 (stop after Dijkstra's) Optional: Path-finding tutorial (stop at "Heuristic search") |
slides |
| Nov. 30/Dec. 1 | NO LAB THIS WEEK | ||||
| 26 | Dec. 5 | Graphs | Shortest path: Dijkstra's algorithm | ||
| 27 | Dec. 7 | Sorting (abbreviated) | Insertion sort, mergesort | Drozdek 9.1.1, 9.3.4 Optional: Sorting algorithms animated |
|
| Dec. 7/Dec. 8 | NO LAB THIS WEEK | ||||
| 28 | Final review on YouTube | Final review slides Post-midterm lecture notes |
recording (with solutions to 2010 final linked below) 2010 final (ignore Q4 and Q5, but see Q2 on 2010 midterm) | ||
| Dec. 12 -- this is past the end of classes | Final project demos | Project due | |||
| Dec. 13-17 | FINAL EXAM | Time and location??? |
