CS 218 Fall 2025¶
Course Information¶
Welcome to CS 218 Fall 2025 at UC Riverside! The goal of this course is to learn and explore data structures, 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
During lecture we will introduce the theoretical algorithmic and analytics topics and apply these topics with demonstration design and analysis questions. While lecture is very important, practice with homework will solidify the theory and practical application.
Please find the pdf of the class syllabus here.
** Lecture **
Prof. Mingxun Wang -
5:00-6:20 PM Tuesday/Thursday
Materials Science and Engineering 103
** Instructional Personnel **
Prof. Mingxun Wang - mingxun.wang@cs.ucr.edu
Office Hours: Tuesday/Thursday 3:00-3:30PM - MRB 4122
TA Yourae Shin - yshin062@ucr.edu
Office Hours: Monday and Wednesday 2:00-3:00pm – MRB 4th floor kitchenette
Prerequisite¶
CS 141 – Undergraduate Intermediate Data Structures and Algorithms or equivalent
Class Communication¶
Canvas will be used for all for class communication and announcements. If you need to contact a TA or Professor, please use email listed above
Course Reading Materials¶
Introduction to Algorithms (CLRS).
Third Edition. Cormen, Leiserson, Rivest, and Stein. MIT Press.
Course Schedule¶
| Lecture # | Date | Subject |
|---|---|---|
| 1 | 9/25/2025 - Thursday | Introduction |
| 2 | 9/30/2025 - Tuesday | Analysis of Algorithms |
| 3 | 10/2/2025 - Thursday | Lower Bound Analysis |
| 4 | 10/7/2025 - Tuesday | Divide and Conquer |
| 5 | 10/9/2025 - Thursday | Divide and Conquer |
| 6 | 10/14/2025 - Tuesday | Greedy |
| 7 | 10/16/2025 - Thursday | Greedy |
| 8 | 10/21/2025 - Tuesday | Data Structures |
| 9 | 10/23/2025 - Thursday | Dynamic Programming |
| 10 | 10/28/2025 - Tuesday | Dynamic Programming |
| 11 | 10/30/2025 - Thursday | Dynamic Programming |
| 12 | 11/4/2025 - Tuesday | Dynamic Programming |
| 13 | 11/6/2025 - Thursday | Randomized Algorithms |
| 14 | 11/11/2025 - Tuesday | Randomized Algorithms |
| 15 | 11/13/2025 - Thursday | Midterm |
| 16 | 11/18/2025 - Tuesday | Graphs |
| 17 | 11/20/2025 - Thursday | Graphs |
| 18 | 11/25/2025 - Tuesday | Graphs |
| 19 | 11/27/2025 - Thursday | Thanksgiving Break |
| 20 | 12/2/2025 - Tuesday | Graphs |
| 21 | 12/4/2025 - Thursday | Review and End of Class Party |
Slides will be made available at the following links after the lecture is given.
Homework¶
All written homework, homework solutions, and grades will be posted to UCR's Canvas Online System. We will have 5 homework sets in this course, released 2 weeks apart.
You must submit your solutions (in pdf format generated by LaTeX) via GradeScope. Canvas submissions will not be accepted. You are expected to understand any source you use and solve problems on your own. You may be asked questions about your solutions to ensure that you understand them.
Homework will be due at 7:00 AM on the due date.
| Release Date | Due Date | Description |
|---|---|---|
| 9/29/2025 | 10/14/2025 | HW1 |
| 10/13/2025 | 10/28/2025 | HW2 |
| 10/27/2025 | 11/11/2025 | HW3 |
| 11/10/2025 | 11/25/2025 | HW4 |
| 11/24/2025 | 12/4/2025 | HW5 |
We understand that sometimes unexpected events happen and in turn we will drop your lowest homework score. As such, we will not accept late homework under any circumstances, extenuating or not.
Homework Integrity 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 (LLMs included)
- Read or look up others’ solutions in this course
- Share your solutions with any other students during or after the completion of this course
It’s acceptable 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 (either a full chat log or a link to the chat). Please note, that in general we do not recommend you use these AI-based resources as you will likely get a weaker grasp on how to solve these problems and allow you to complete the homework with a shallow understanding of the theoretic and practical application of the material. This will hurt you in the long run because homework is not worth much of your grade but you will do not do as well on your exams (which are worth significant portions of your grade).
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 plagiarized 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 (15%)
- One mid-term exam (40%) - In Class
- Final inclusive exam (45%) - Wednesday, December 10, 3:00 p.m. - 6:00 p.m.
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. Regrade requests that lack substantial justification will be summarily rejected with no chance for a rebuttal. 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 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 assignment and take away credit from other assignments. All violations of this collaboration policy will be reported to the university.