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Course Information

Repository Guide | English | 한국어

Last Updated: 2026-10-27

Table of Contents


1. Instructor and Research Lab

  • Instructor: Prof. Unggi Lee, Department of Computer Science and Software Engineering
  • Research Lab: LEAP Lab, focusing on generative AI in education, pedagogical alignment, large language models, and knowledge tracing.

2. Schedule and Class Format

  • Credits: 3
  • Class Times: Tuesday periods 8-9, Thursday period 7
  • Classroom: Science and Technology Building 2, Room 310
  • Weekly Format: A quiz and the lecture (part 1) in the 1st period, the lecture (part 2) in the 2nd period, and a lab in the 3rd period.

3. Assessment

Item Weight
Assignments (quizzes 5%, homework 5%) 10%
Midterm exam (written) 30%
Final exam (term project) 30%
Final exam (written) 30%
Attendance 0%
  • Quizzes are held at the start of the first period in weeks 3-7 and 9-13, cover the previous week's material, and may appear on written exams.
  • There are five homework assignments in weeks 2-6, each building a web application that applies the algorithms learned that week.
  • Written exams are handwritten and last one hour.
  • The term project part of the final exam is a team-based term project carried out in weeks 9-13. Teams of 3-4 build a web application that applies all algorithms covered in class and submit the working application, a report, and a presentation as deliverables.

4. Course Policies

  • Generative AI is prohibited during quizzes.
  • Generative AI tools such as coding agents are permitted and actively encouraged in labs, assignments, and the term project, and in assignments students must clearly explain their own reasoning and design choices.
  • A grade is not awarded if a student misses more than one third of the total class hours.

5. Weekly Plan and Progress

Week Class Dates (Tue, Thu) Planned Topic Lecture Decks Covered
W01 3/3, 3/5 Introduction to Algorithms W01_LEC_Introduction-to-Algorithms
W01_LAB_Coding-Agents
W02 3/10, 3/12 Algorithm Design and Complexity Analysis W02_LEC_Algorithm-Design-and-Complexity-Analysis
W02_LAB_Complexity-Analysis-Practice
HW1
W03 3/17, 3/19 Arrays, Stacks, Queues, and Basic Sorting Algorithms W03_LEC_Arrays-Stacks-Queues-and-Basic-Sorting-Algorithms
W03_LAB_Sorting-Algorithm-Implementation-and-Benchmark
HW2
W04 3/24, 3/26 Divide and Conquer Algorithms W04_LEC_Divide-and-Conquer-Algorithms
W04_LAB_Advanced-Divide-and-Conquer
HW3
W05 3/31, 4/2 Greedy Algorithms W05_LEC_Greedy-Algorithms
W05_LAB_Greedy-Algorithms
HW4
W06 4/7, 4/9 Dynamic Programming W06_LEC_Dynamic-Programming
W06_LAB_Dynamic-Programming
HW5
W07 4/14, 4/16 Review and Problem Solving W07_LEC_Midterm-Review
W07_LAB_Midterm-Exam-Preparation
W08 4/21, 4/23 Midterm Exam -
W09 4/28, 4/30 Search Trees W09_LEC_Search-Trees
W09_LAB_Claude-Code-Tutorial-and-Project-Kickoff
W10 5/5, 5/7 Hash Tables and Set Data Structures W10_LEC_Hash-Tables-and-Set-Data-Structures
W10_LAB_Hash-Tables-and-Project
W11 5/12, 5/14 Graph Algorithms I W11_LEC_Graph-Algorithms-I
W11_LAB_Graph-Traversal-and-Project
W12 5/19, 5/21 Graph Algorithms II W12_LEC_Graph-Algorithms-II
W12_LAB_Shortest-Paths-and-Project
W13 5/26, 5/28 NP-Complete Problems and Approximation Algorithms W13_LEC_NP-Completeness-and-Approximation-Algorithms
W13_LAB_Project-Finalization-and-Presentations
W14 6/2, 6/4 Final Exam (Term Project) W14_LEC_Approximation-Algorithms
W15 6/9, 6/11 Final Exam (Written) -
W16 6/16, 6/18 Study Week -
  • The planned topics follow the weekly plan in the syllabus.
  • The theory decks (W##_LEC_) are covered in the lecture note of the Week## unit with the same number, and the lab decks of weeks 1-6 (W##_LAB_) in the lab note of the same unit. The units are listed in the Lecture-Notes index.
  • The week 7 decks have no unit of their own, and the midterm exam preparation materials are in the Coursework index.
  • The lab decks of weeks 9-13 accompany the team-based term project carried out in the lab sessions, so they have no lab notes.
  • HW1 to HW5 are the homework assignments given once a week in weeks 2-6, and their solutions and reports are in the Coursework index.

6. References

Type Contents
Textbook T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed. Cambridge, MA, USA: MIT Press, 2009. (CLRS)
Lecture Materials Instructor's Markdown notes and slides (GitHub)
Coding Practice Baekjoon, Programmers, LeetCode, Codeforces, solved.ac
Algorithm Visualizations VisuAlgo, Data Structure Visualizations