CS 161 - Design and Analysis of Algorithms

CS 161 - Design and Analysis of Algorithms - Winter, 2026

Course staff

Instructor:

Ioannis Panageas

Email: ipanagea at ics dot uci dot edu.
Office hours: Wednesday 02:00 - 04:00pm, by appointment

Teaching Assistants:

Rohan Chauhan (Email for questions related to discussions)

Email: rmchauha at uci dot edu.
Office hours: TBA

Nikolas Patris (Email for regrading requests)

Email: npatris at uci dot edu.
Office hours: TBA, via zoom

Jingming Yan (Email for any other requests)

Email: jingmy1 at uci dot edu.
Office hours: TBA

Class announcements

Class announcements will be made on canvas and Edstem. Please check there frequently.

Class meetings

Lectures: 12:30 - 01:50pm TuTh EH 1200

Midterms will be given during the lecture time.

Final: March 20, between 10:30am - 12:30pm

Discussion:

03:30 - 04:50pm MW ELH 100

Edstem - Gradescope:

Questions of general interest about the course material, the homework, and the tests should be posted on Edstem.
Regrade requests on the midterm will be handled by TAs on Gradescope. For any other requests you need first to send an email to Jingming Yan.

Syllabus

The syllabus, including grading policy, schedule of hws/exams and academic honor code can be found here.

Textbook

Required textbook. [GT] Algorithm Design and Applications, by Michael T. Goodrich and Roberto Tamassia.
Recommended textbook. [CLRS] Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.

Schedule of classes and slides

Lecture no.	Topics	Slides (PDF)	Slides (PPT)	Reading - Comments
Lecture 1 (1/6)	Administrivia, Introduction	L1	L1
Lecture 2 (1/8)	Review of basic concepts			[GT] Sections 1.1, 1.2, 1.3, Chapter 2, Sections 3.1, 5.2.2
Lecture 3 (1/13)	Divide and conquer, mergesort, counting intersections			[GT] Sections 8.1, 8.2, 11.1, 11.2, 11.3, 11.4
Lecture 4 (1/15)	Divide and conquer (continued)			[GT] Sections 8.1, 8.2, 11.1, 11.2, 11.3, 11.4
Lecture 5 (1/20)	Divide and conquer (continued)			[GT] Sections 8.1, 8.2, 11.1, 11.2, 11.3, 11.4
Lecture 6 (1/22)	Divide and conquer (continued)			[GT] Sections 8.1, 8.2, 11.1, 11.2, 11.3, 11.4
Lecture 7 (1/27)	Midterm 1
Lecture 8 (1/29)	Dynamic Programming: Introduction. Memoization, Rod cutting and 0/1 Knapsack problems			[GT] Chapter 12
Lecture 9 (2/3)	Dynamic Programming: 0/1 Knapsack (cont.), Bellman-Ford, Interval Scheduling			[GT] Chapter 12
Lecture 10 (2/5)	Dynamic Programming (cont.)			[GT] Chapter 12
Lecture 11 (2/10)	Midterm 2
Lecture 12 (2/12)	Max flow, bipartite matching			[GT] Sections 16.1, 16.2, 16.3, 16.4
Lecture 13 (2/17)	Max flow, bipartite matching, further examples			[GT] Sections 16.1, 16.2, 16.3, 16.4
Lecture 14 (2/19)	Greedy method, Fractional Knapsack			[GT] Sections 10.1, 10.2
Lecture 15 (2/24)	Minimum spanning trees			[GT] Sections 15.1, 15.2, 15.3
Lecture 16 (2/26)	More problems on greedy			[GT] Sections 10.1, 10.2
Lecture 17 (3/3)	Midterm 3
Lecture 18 (3/5)	NP-completeness, reductions			[GT] Chapter 17, NOT part of the final exam
Lecture 19 (3/10)	Recap			Dynamic Programming, Divide and Conquer
Lecture 20 (3/12)	Recap			Greedy, Maxflow, MSTs

Homework Assignments

The homework assignments will be posted in Edstem, Canvas and Gradescope.