CS 218 Fall 2024¶
Course Information¶
Welcome to CS 218 Fall 2024 at UC Riverside!
The goal of this course is to learn and explore data structures and basic algorithms design and analysis. It will cover the following topics:
- Algorithms analysis
- Lower Bound analysis
- Divide-and-conquer algorithms
- Greedy algorithms
- Dynamic programming
- Randomized algorithms
- Graph data structures and algorithms
Lecture
- Prof. Mingxun Wang - 11:00-12:20 PM TTh - Materials Science and Engineering 103
Office Hours
- Prof. Mingxun Wang - Tuesday/Thursday 12:20 PM - 1PM - MRB 4122
- TA Michael Strobel - Tuesday/Friday 4:00 PM - 4:50PM - MRB 4th Floor Break Room
Prerequisite(s): CS 141 or equivalent
Logistics¶
All written homework, homework solutions, and grades will be posted to UCR's Canvas Online System.
Instructional Personel¶
- Prof. Mingxun Wang - mingxun.wang@cs.ucr.edu
Teaching Assistants
- Michael Strobel (mstro016@ucr.edu)
Class Communcation¶
We will use Canvas for class communication for announcements. If you need to contact a TA or Professor, please use email.
Reading Materials¶
Introduction to Algorithms (CLRS).
Third Edition. Cormen, Leiserson, Rivest, and Stein. MIT Press.
Course Schedule¶
| Lecture # | Date | Subject | Slides |
|---|---|---|---|
| 1 | 9/26/2024 - Thursday | Introduction | - |
| 2 | 10/1/2024 - Tuesday | Analysis of Algorithms | - |
| 3 | 10/3/2024 - Thursday | Lower Bound Analysis | - |
| 4 | 10/8/2024 - Tuesday | Divide and Conquer | - |
| 5 | 10/10/2024 - Thursday | Divide and Conquer | - |
| 6 | 10/15/2024 - Tuesday | Greedy | - |
| 7 | 10/17/2024 - Thursday | Greedy | - |
| 8 | 10/22/2024 - Tuesday | Data Structures | - |
| 9 | 10/24/2024 - Thursday | Dynamic Programming | - |
| 10 | 10/29/2024 - Tuesday | Dynamic Programming | - |
| 11 | 10/31/2024 - Thursday | Dynamic Programming | - |
| 12 | 11/5/2024 - Tuesday | Dynamic Programming | - |
| 13 | 11/7/2024 - Thursday | Randomized Algorithms | - |
| 14 | 11/12/2024 - Tuesday | Randomized Algorithms | - |
| 15 | 11/14/2024 - Thursday | Midterm | - |
| 16 | 11/19/2024 - Tuesday | Graphs | - |
| 17 | 11/21/2024 - Thursday | Graphs | - |
| 18 | 11/26/2024 - Tuesday | Graphs | - |
| 19 | 11/28/2024 - Thursday | Thanksgiving Break | - |
| 20 | 12/3/2024 - Tuesday | Graphs | - |
| 21 | 12/5/2024 - Thursday | Review and End of Class Party | - |
Student Resources (Slides and other resources)¶
Slides will be made available at the following links after the lecture is given.
- Wang: All Slides
Assignments¶
Homeworks will be posted on Canvas.
You must submit your solutions (in pdf format generated by LaTeX) via GradeScope. Canvas will not be accepted.
Homework will be due at 7:00 AM on the due date.
| Release Date | Due Date | Description |
|---|---|---|
| 10/1/2024 | 10/16/2024 | HW1 |
| 10/15/2024 | 10/30/2024 | HW2 |
| 10/29/2024 | 11/13/2024 | HW3 |
| 11/12/2024 | 11/27/2024 | HW4 |
| 11/26/2024 | 12/5/2024 | HW5 |
Homework Policies¶
You can get help from the instructor and TA. You can also get help from textbooks (or relevant books), the Internet, or discussions with your classmates, but you must cite them fully and completely (i.e., provide citations to the book or website link, acknowledge the other students that had discussions with you). However, you are NOT allowed to:
- Copy anything from the book or the internet
- Read or look up other's solutions in this course
- Share your solutions with any other students during or after the compleition of this course
It’s OK to get inspirations from other sources, and citing the sources does not affect your grade. However, using any source without citing them will be treated as cheating and will result in unfavorable outcomes.
If you use any AI-based resources (e.g., ChatGPT or other LLMs), you need to provide the full conversation with it to clearly specify what kind of help you received from it.
When you write down your solution, it MUST be close-book. This is to make sure you truly understand and can recreate the solutions.
NOTE: If you share your solutions with others in the course and they turn in a plagarized copy of your answer, we will not distinguish who was the source or the recipient of the material, both parties will be penalized.
Course Work/Grade Breakdown¶
- Five homework assignments (30%)
- One mid-term exams (30%)
- Final inclusive exam (40%)
- Mingxun Wang - Tuesday, December 10, 8:00 a.m. - 11:00 a.m.
Assignment Policy¶
- You will have 3 grace days that can be used throughout the quarter to extend a homework deadline. You must notify the TA via email to have the submission repoened.
- Grace days must be requested before the assignment deadline (or before the deferred deadline if a grace day was previously applied for that assignment).
- Unless mentioned otherwise, late submissions are not accepted after the due date at 7:00AM PST.
- Assignments should be computer-typed and submitted on GradeScope.
- If you are using any external source, you must cite it and clarify what exactly got out of it.
- You are expected to understand any source you use and solve problems in your own.
Regrade Policy¶
Homework/Exam regrades can be submitted via Gradescope within 3 days of the initial release of scores. Regrades should be question-specific and offer substantial written justification for the request. For any regrade request, we reserve the right to regrade the entire assignment.
Academic Integrity¶
We have the highest standards and expectations for academic integrity. Please refer to the UCR Guidelines. Sanction Guidelines, Academic integrity Guidelines.
Any work submitted as a homework assignment or examination must be entirely your own and may not be derived from the work of others, whether a published or unpublished source, the worldwide web, another student, other textbooks, materials from another course (including prior versions of this course), or any other person or program. You may not copy, examine, or alter anyone else’s homework assignment or computer program, or use a computer program to transcribe or otherwise modify or copy anyone else’s files. It is not acceptable to look at exams or homework assignment solutions from prior quarters.
It is not acceptable to share your solutions or codes with your friends, or anyone else (other than the course staffs) without the permission of the instructors. You are not helping your friends by doing so. It is not acceptable to read other students’ solutions or code. You cannot share the course material (e.g., exams, homework assignments and solutions) with anyone else without the permission of instructors after you have completed the course.
Penalties may be assessed after you have completed the course, and some requirements of the collaboration policy (such as restrictions on you sharing your solutions and standard solutions) extend beyond your completion of the course.
The minimum penalty for cheating (including plagiarism from others) will be a zero grade for the whole assignment; a typical penalty will be at minimum a -100% on the assignment - this will result in worse of a penalty than a 0 on the assigment and take away credit from other assignments. All violations of this collaboration policy will be reported to the university.