CS 141 Winter 2024¶
Course Information¶
Welcome to CS 141 Winter 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 (asymptotic notation & recurrence relations)
- Divide-and-conquer algorithms
- Greedy algorithms
- Dynamic programming
- Graph data structures and algorithms
- Advanced topics in algorithms analysis and design: NP-Completeness
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Lecture
- Prof. Mingxun Wang - 3 PM MWF
- Prof. Silas Richelson - 11 AM MWF
Discussion
Note: Please attend your assigned discussion section as there is limited seating.
Office Hours
- Prof. Mingxun Wang - Monday 1PM - 2PM - MRB 4122
- Prof. Silas Richelson - Tuesday 11AM - 12PM - WCH 331
- TA Jordan Kuschner - Thursday 2PM - 3PM - WCH 110
- TA Ziyang Men - Wednesday 10AM - 11AM - WCH 110
- TA Xianghu Wang - Friday 1030AM - 1130AM - MRB 3rd Floor Balcony
Prerequisite(s): CS 014 with a grade of “C-” or better; CS 111; MATH 009C or MATH 09HC; proficiency in C++.
Logistics¶
All written homework, homework solutions, and grades will be posted to UCR's Canvas Online System.
Instructional Personel¶
- Prof. Silas Richelson - my_first_name@cs.ucr.edu
- Prof. Mingxun Wang - mingxun.wang@cs.ucr.edu
Teaching Assistants
- Jordan Kuschner (jordan.kuschner@email.ucr.edu) - Head TA
- Xianghu Wang (xwang473@ucr.edu)
- Ziyang Men (ziyang.men@email.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.
Prerequisite knowledge in the book that will not be taught in this course: Please make sure you understand Sections 6, 10-13, and 22.1-22.3 in the book.
Course Schedule¶
| Date | Subject | Slides |
|---|---|---|
| 1/8/2024 - Monday | Introduction | - |
| 1/10/2024 - Wednesday | Analysis of Algorithms | - |
| 1/12/2024 - Friday | Analysis of Algorithms | - |
| 1/15/2024 - Monday | MLK Day | - |
| 1/17/2024 - Wednesday | Analysis of Algorithms | - |
| 1/19/2024 - Friday | Divide and Conquer | - |
| 1/22/2024 - Monday | Divide and Conquer | - |
| 1/24/2024 - Wednesday | Divide and Conquer | - |
| 1/26/2024 - Friday | Dynamic Programming | - |
| 1/29/2024 - Monday | Dynamic Programming | - |
| 1/31/2024 - Wednesday | Dynamic Programming | - |
| 2/2/2024 - Friday | Midterm Exam 1 | - |
| 2/5/2024 - Monday | Dynamic Programming | - |
| 2/7/2024 - Wednesday | Dynamic Programming | - |
| 2/9/2024 - Friday | Dynamic Programming | - |
| 2/12/2024 - Monday | Greedy Algorithms | - |
| 2/14/2024 - Wednesday | Greedy Algorithms | - |
| 2/16/2024 - Friday | Greedy Algorithms | - |
| 2/19/2024 - Monday | President's Day | - |
| 2/21/2024 - Wednesday | Greedy Algorithms | - |
| 2/23/2024 - Friday | Midterm Exam 2 | - |
| 2/26/2024 - Monday | Greedy Algorithms / Graphs | - |
| 2/28/2024 - Wednesday | Graphs | - |
| 3/1/2024 - Friday | Graphs | - |
| 3/4/2024 - Monday | Graphs | - |
| 3/6/2024 - Wednesday | Revisiting Dynamic Programming | - |
| 3/8/2024 - Friday | Revisiting Dynamic Programming | - |
| 3/11/2024 - Monday | Revisiting Divide and Conquer | - |
| 3/13/2024 - Wednesday | Revisiting Greedy Algorithms | - |
| 3/15/2024 - Friday | Review Problems | - |
Student Resources (Slides and other resources)¶
Slides will be made available at the following links after the lecture is given.
- Section 1 (Richelson): All Slides
- Section 2 (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 |
|---|---|---|
| 1/8/2024 | 1/23/2024 | HW1 |
| 1/22/2024 | 2/6/2024 | HW2 |
| 2/5/2024 | 2/20/2024 | HW3 |
| 2/19/2024 | 3/5/2024 | HW4 |
| 3/4/2024 | 3/16/2024 | HW5 |
Course Work/Grade Breakdown¶
- Participation Activities (3%)
- Five homework assignments (40%)
- Two mid-term exams (32%)
- Final inclusive exam (25%)
- Silas Richelson - TBD
- Mingxun Wang - Tuesday, March 19, 3:00 p.m. - 6:00 p.m.
Assignment Policy¶
- Unless mentioned otherwise, late submission allowed for 20% penalty for each calendar day, with the calendar day rolling over 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.
Academic Integrity¶
We have the highest standards and expectations for academic integrity. Please refer to the UCR 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 or using ML tools such as chatGPT) 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.