CS5800 Algorithms Section 3, Fall 2026

about CS5800 home schedule gradescope piazza

Instructor: Virgil Pavlu E-mail: v.pavlu@northeastern.edu, but I prefer Teams-chat

Lecture: Mon+Wed 2:50-4:30 pm, BK 310
Office hours (queue, first-come; TAs may leave after an hour if empty):
Boston campus: Mon 7:30 pm, HA-321; Wed 7:30 pm, RY-431; Thu 6:00 pm, HS-107

Tentative schedule:

5800 TA Schedule
Time	Mon	Tue	Wed	Thu	Fri	Sat
9:00
9:30
10:00				Rohil - HDEMO
10:30				Rohil - HDEMO
11:00	Aashish-LLM	Rohil - HDEMO	Aashish-LLM	Rohil - HDEMO	Alex - HQW	Alex - HQW flex time
11:30	Aashish-LLM	Rohil - HDEMO	Aashish-LLM	Rohil - HDEMO	Alex - HQW	Alex - HQW flex time
12:00	Aashish-LLM	Rohil - HDEMO	Aashish-LLM	Shreyas Lecture QA	Alex - HQW	Alex - HQW flex time
12:30	Aashish-LLM	Rohil - HDEMO	Aashish-LLM	Shreyas Lecture QA	Alex - HQW	Alex - HQW flex time
1:00				Shreyas Lecture QA
1:30				Shreyas Lecture QA	Divyam - HQW
2:00		Sanjana-LLM		Sanjana-LLM	Divyam - HQW	Samarth - HQW flex time
2:30	CLASS	Sanjana-LLM	CLASS	Sanjana-LLM	Divyam - HQW	Samarth - HQW flex time
3:00	CLASS	Sanjana-LLM	CLASS	Sanjana-LLM	Divyam - HQW	Samarth - HQW flex time
3:30	CLASS	Sanjana-LLM	CLASS	Samarth, Sanjana	Henry - HQW	Samarth - HQW flex time
4:00	CLASS		CLASS	Samarth - HDEMO	Henry - HQW
4:30	CLASS		CLASS	Samarth - HDEMO	Henry - HQW
5:00				Samarth - HDEMO
5:30
6:00		Rohil - HDEMO		HS107 Alex, Henry
6:30		Rohil - HDEMO		HS107 Alex, Henry
7:00		Rohil - HDEMO		HS107 Alex, Henry
7:30	HA321 Divyam, Aashish/Sanjana	Rohil - HDEMO	RY431 Henry
8:00	HA321 Divyam, Aashish/Sanjana		RY431 Henry
8:30	HA321 Divyam, Aashish/Sanjana		RY431 Henry
9:00

TAs:

Henry Abrahamsen, abrahamsen.h@northeastern.edu
Divyam Gupta, gupta.divyam@northeastern.edu
Sanjana Sri Injamuri, injamuri.s@northeastern.edu
Samarth Jain, jain.samar@northeastern.edu
Rohil Kulshreshtha, kulshreshtha.r@northeastern.edu
Alexandria Stevenson, stevenson.al@northeastern.edu
Rama Krishna Aashish Tummuri, tummuri.r@northeastern.edu
Shreyans Mulkutkar, mulkutkar.s@northeastern.edu

Teams : Khoury - CS 5800 3 (Fall 2026)

Not required, but some OH/recitations, code demos, or even lectures might happen online if necessary.

Discussion : piazza

Please use the discussion forum for all questions regarding material, assignments, due dates, data issues, programming issues, etc. That is, do not use the direct email to TAs or Instructors for these questions.
Personal/private matters, such as availability, delays, grades, term projects or other advanced material, etc, should be in Teams private chat with instructor or course coordinator

Gradescope: https://www.gradescope.com/courses/1388020

HW Submission must be latex compiled pdfs. Code can be submitted as a file (when so indicated) or in general as printed into the pdf HW submission. Homework is due on Fridays.

Text Book Required

[CLRS] Algorithms 4th ed by Cormen, Leiserson, Rivest, Stein

Other texts

Kleinberg, Tardos: Algorithms Design

Algorithms by S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani

Algorithms by Robert Sedgewick, Kevin Wayne

Prerequisites

Basic imperative programing experience in a language such as Python, Java, C, Matlab. You will actually not do much programing in the course, but a minimum experience with the logic is required.
Math (general): arithmetic, logs, exponential, calculus, linear algebra
Discrete Math
An UG-level intro to Algorithms/Datastructures Course

Homework

There will be about 12 assignments, including programming assignments; see the schedule for exact dates. Each homework has two required parts:
- No-credit problems: These problems are automatically graded to provide feedback. They do not earn course credit, but they must still be submitted.
- Credit problems: These problems must be submitted and discussed with a TA during office hours to receive credit.
- Code demonstrations: All submitted code must run successfully and must be demonstrated to a TA to receive credit.
You may discuss homework problems with classmates and form study groups. However, each student must understand the problems and write their own solutions and code. Do not give a solution to another student, and do not read or copy another student's solutions or code. At the beginning of your write-up, acknowledge everyone with whom you discussed the problems.
We expect the average total workload to be 20 hours per week. Some students may need additional time for prerequisite review. Extra-credit and optional problems are harder, more time-consuming, and worth no points; they are for students who want to explore beyond the required material. The best way to maximize your grade is to complete the regular credit work on time.
Late homework: Homework is due through Gradescope on the announced Friday deadline. Late work is normally not accepted. If you have a valid reason for a late submission, contact the instructor in advance to request an exception.

Use of LLMs and Generative AI

Except when an assignment explicitly directs you to use an LLM, the following policy applies:

Not allowed: Using an LLM to solve an assigned problem, discover or derive the correct answer, or produce a full or substantial portion of a solution write-up.
Allowed: Using an LLM to check an answer you have already developed; identify or explain possible mistakes without supplying a replacement solution; help locate bugs in code or pseudocode you wrote; polish text you already wrote; redraw or clean up your hand-created illustrations; or make presentation-level edits to content and plots you already created.
The reasoning, solution, and substantive content of submitted work must remain your own.

Exams

There will be two in-class exams on paper. The midterm will be on a Saturday; its date, time, and room are TBD. The final exam date, time, and room are TBD. Each exam will have about 5–6 problems, and you should reserve the whole day.
Exams are open book and open notes. You may use your homework and any other class materials.
No internet-capable devices may be used during an exam. Bring any materials you want to consult in printed form.

Grading

LLM assignments: 15%
Homework credit problems: 15%
Midterm exam: 35%
Final exam: 35%

Students may earn up to an additional 5% for being exceptionally active in office hours, especially by helping other students work through difficult problems. These weights are subject to slight adjustment, and grades may be curved.

Academic Honesty

All work submitted for credit must be your own.
You may discuss the homework problems or projects with your classmates, the teaching assistant(s), and instructor. You must acknowledge the people with whom you discussed your work, and you must write up your own solutions. Any written sources used (apart from the text) must also be acknowledged; however, you may not consult any solutions from previous years' assignments whether they are student or faculty generated.