COMP 160: Introduction to Algorithms (Fall 2017)

On this page:
When and where is class?
Who is teaching?
Who are your TAs?
Who should you contact?
Textbook, prerequisites, topics
Grading (and an important note)
How to submit homework
Tips on doing well
Tufts policies, and the consequences of academic dishonesty

When and Where:
  • Time: Tuesdays and Thursdays, 6:00 - 7:15 pm     (N+ block)
  • Location: Robinson 253

  • Review sessions (non-mandatory): Mondays at noon; Wednesdays at noon and/or around 7:30. Weekly updates will appear in the TA schedule on the main page.

Instructors: Office hours will be posted on the main page.

  • Office hours may vary and will be updated weekly on the main page.
  • Unless noted otherwise, office hours are held in the collaboration room on the 2nd floor of Halligan.
  • Besides the exceptions listed below, email addresses are:       firstname.lastname@tufts.edu
Click on a TA name below to see their photo.
Ben Jiang          (Zhongjian.Jiang@tufts...)
Ning Zhu         
Arezoo Sadeghi          (arezu.sadeghi@gmail...)
Brian Rappaport         
Cihan Sebzeci         
Duc Nguyen          (manh.nguyen@tufts...)
Eric Chen          (eric.chen595575@tufts...)
Erika Odmark
Evgeni Dobranov
Harrison Kaiser         
Kalina Allen         
Larry Zhang          (lawrence.zhang@tufts...)
Matt Jones          (matthew.jones@tufts...)
Minh D. Nguyen          (minh_d.nguyen@tufts...)
Tommy Tang          (chao.tang@tufts...)
Yuki Zaninovich         
Zach Kirsch          (zachary.kirsch@tufts...)

Who to contact, and when:

Textbook: Introduction to Algorithms, 3rd edition, by Cormen, Leiserson, Rivest and Stein.
This is commonly just referred to as ``CLRS". More info at
MIT press.
Note: the book is massive. We will not cover everything. See "Topics" below.

Prerequisites: COMP 15 and COMP/MATH 61 (in other words, basic data structures and discrete math)
If you have not passed both of these courses and are enrolled in comp160, you must contact Greg.

Topics: This is an introduction to the design and analysis of algorithms, which involves discussing a few basic data structures as well. Many topics could fit in such a course, and not all intro courses go over exactly the same material.
We will place all emphasis on theory instead of programming.
To see what is taught in this course, please visit the Reading page.


If you have a serious reason for not submitting homework or not taking an exam, you should notify your Dean and/or Health Services, and of course you may CC us as well.
We cannot arrange a makeup exam if you have a predictable conflict that could be reasonably avoided. Check the exam schedule before making travel arrangements.

Important note: do not assume that the numerical score you receive on homework or exams directly corresponds to a particular letter grade. Your score is just a number that lets us figure out who is doing better or worse than average. Don't panic if you think you scored low. An evaluation of your performance will be given after the first exam: you will see a plot of exam scores and a letter grade estimate. You will also receive updates about average homework performance.
Homework scores usually average around 85%. Exam scores are typically low; the median is usually around 60%, and an 80% is excellent.

How to submit homework

Tips for doing well in this course, if you find it challenging:

Students and instructors of this course are to respect the following:

Don't cheat: