• This course is online. If there are enough available seats and the university permits it, you may attend lectures for my undergraduate Algorithms course on campus: Mondays and Wednesdays, 5 - 6:20 pm, Dibner Building, Rm LC400 (Note: the system formerly showed JABS 474).
• The course is asynchronous. This means that in order to learn the material you will first watch pre-recorded videos. We will have regular meetings to follow up, but I will assume that you have already put in significant time watching the videos. The intention is to spend our meeting time discussing the deeper concepts, and illustrating additional examples, rather than going over the basics from scratch. I will also allocate time to review basic concepts, as needed by students.
• Zoom links for all office hours / review sessions will be posted in EdStem.
• On the Resources page you can find pre-recorded (and edited) lectures from recent semesters. There are also screen recordings of the lecture notes, with narrations.
  You are expected to keep up with the schedule that can be found on the course Schedule page.
• I will not record live lectures or Zoom meetings, but if we discuss any new practice problems they will be posted in EdStem.

Non-required 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: my Resources page refers to this book so you can find relevant chapters if you wish. The course doesn't explicitly rely on the book though. Also, CLRS is massive. We will not cover everything in it. See "Topics" below.

Prerequisites: Discrete math is important. Basic understanding of some data structures and probability will matter too. Overall, you should possess mathematical maturity.
See the Resources page and search for the word "prerequisite" to see when you will need to know background material.
You will probably find this course difficult if you're not comfortable with induction, recursion and proofs, or if big-O seems like a challenging concept. It is assumed that you know the basics about arrays, linked lists and trees. Knowledge of basic probability will be helpful for two or three lectures but I will recap what you need to know.
If you have not done well in discrete math or have not passed data structures, you should seriously consider taking your time to create a solid background before taking this course.

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. This course is about figuring out how to solve a problem before you start coding.
To see what is taught in this course, please visit the Resources page.

• In Brightspace you will find a link to enrol in our EdStem page. Once you have enrolled, you can use this link. Your name in EdStem should contain your first and last name as they appear in Albert. Feel free to add a nickname in parentheses as well, but your official name should be there too.
• Before posting a question in EdStem, make sure that it hasn't already been answered (either in EdStem itself, or on the course website).
• If you have a question that does not affect other students, or that may reveal answers to homework or practice questions before solutions are posted, please use my course email address instead. Do not post questions like "I'm trying to solve homework X like this but I'm not sure it's correct". That kind of question should be asked during office hours, after others confirm that they don't mind.
• In general, do not use EdStem as a substitute for sending me private messages that relate only to your own situation.
• I highly recommend setting EdStem email preferences so that you receive notifications instantly, or at least relatively frequently. It is your responsibility to be aware of any updates that I make.

Grading (exams)

• We will have 3 exams, including one during the final exam week. Each is worth a third of the final grade, and covers approximately a third of the course material (non-cumulative). Each exam will last approximately 80 minutes.
  Note that these exams are on campus. If you have a significant conflict, email me before the course begins.
  1. Tuesday, October 25, 12:30 - 1:50pm
  2. Tuesday, November 22, 12:30 - 1:50pm
  3. TBD, final exam block
• On exams you may need to describe algorithms, proofs of correctness, or time complexity analysis, for topics found in the course notes, or for slightly modified versions. You will also need to be familiar with homework problems and other practice problems that are handed out. Occasionally there are questions that require developing algorithms for brand new problems.
• Unless noted otherwise, all exams that are held on campus will be closed book. No notes or devices of any kind, except for something to write with.
• Any form of academic dishonesty on exams will result in an F in the course and a report to administration.
• If we need to have exams online then you will likely need to type your exam answers directly into Gradescope. It will help to become familiar with very basic LaTeX (I will explain and offer assistance if needed).
  
• Homework is worth 0%. If you submit it, we will grade it and provide feedback, but this is done purely so that you can practice and understand where you might have incorrect reasoning. It is not factored into your final grade in any way.
  

Gradescope: How to submit homework. Making regrade requests.

• All homework and other practice problems will posted in EdStem. Solutions will be posted there too.
• Homework can be submitted in Gradescope. The access code can be found in EdStem. It is up to you to sign up, using your full name as it appears in Albert. Feel free to add a nickname as well, but your official name should be there too.
• When submitting homework in Gradescope, there is a straightforward way to tag your pages. You must tell Gradescope which pages of your uploaded file relate to each problem. If you don't do this, we will not evaluate your work. Pages should appear in order and should not be rotated. Leave enough space so we can provide comments. Don't crop pages, and don't submit pages with unusual dimensions. Each problem should be on a different page (but subproblems should appear without breaks). Do not submit blank pages if you haven't solved a problem.
• Ideally your submissions should be typed, but you may also submit exceptionally clear handwritten scanned assignments. If your work is not clear, you will be asked to type the rest of your assignments. Either way, you may include figures, in fact you are encouraged to do so. If the figures are photographs or scans of work done on paper, it is your responsibility to make sure that they are clear, with proper contrast and resolution.
  Summary: please respect the effort that your graders will put into grading your work. There should be no shadows, coffee mugs, pets, body parts, etc, visible in your figures.
  
• You can make regrade requests within Gradescope in a straightforward way, for homework and exams.

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

• Use the full version of the course notes when studying, not the condensed version. Do not rely only on the notes, use video as well.
• When using the notes or videos, don't rush. Pause frequently. If you don't understand something, go back and think about what got you stuck, before proceeding. If the issue remains then get in touch with me or the TAs (or your classmates), in EdStem or office hours.
• Always try to anticipate what is coming next. Try to re-prove things. Derive on your own, rather than verifying what is written. When you simply verify what's there, it is easy to overestimate what you've understood.
• Don't cram. The most common reason students don't do well on the exams in this course is that they think they can learn the material for an exam in two days. Spend time on this course regularly. Many students underestimate how much time is required to truly understand some of these topics.
• Use all resources available, if you have time. Prioritize class notes, the summary PDF document at the top of the Resources page, and the videos. The book can be really helpful for some topics, not so much for others, but in general this is subjective. Try to explore links and demos that are provided, if applicable. Do practice problems from the book, starting with the more basic ones. In many cases you can make your own practice problems too (e.g. draw an arbitrary graph and find shortest paths).
• As much as possible, don't memorize. Instead, understand. I do realize that some basic memory is required in any case. But if you think you need to memorize how algorithms work, why they are correct, or what their time complexities are, then you're not approaching this course correctly and should talk to me.
• Pretend that you will have to teach the material that you're reading, to 100 people. They would be asking you why things work, and you should be able to answer correctly.
• Try to do the homework on your own. If you are entirely stuck, get only basic hints from your classmates and TAs, but don't rush to do this if you've only spent an hour or two on a problem. If you have no idea how to approach a problem, then you probably need to study the basics a bit more: take the time to review the material from the corresponding lectures, and solve some easier exercises on your own. Then think about what tools you have just learned about, and whether they might help. If none of this works, then get some hints. Homework sometimes requires multiple hours of work.
• When stuck solving homework problems, see how you'd handle a made-up example. Think about whether your example is oversimplified, whether you've made unnecessary assumptions, and whether your solution is generalizable (if applicable). Also, sometimes it's a good idea to add extra assumptions, thus making the problem easier to solve, and gradually remove the assumptions.
• Read the homework solutions even if you got a good score. Sometimes graders don't notice subtle mistakes on homework submissions. Reading the solutions might help you avoid repeating such mistakes on an exam. Also, even if your ideas worked, the solutions might have a more efficient idea or description that will end up worth knowing about. In any case, it is your responsibility to be familiar with homework solutions, because they might appear on exams.
• Don't hesitate to talk to me and to the TAs.

Everyone involved in this course is to respect the following:

• NYU academic integrity policy
• NYU non-discrimination policy
• Student code of conduct, excused absence, and other things
• Basically, be nice.

• If you cheat on an exam, you will get an F in the course and a report will be sent to administration.
• It is not acceptable to copy solutions from any source or to distribute or receive solutions.

Moses Center Statement of Disability:

• If you are student with a disability who is requesting accommodations, please contact New York University's Moses Center for Students with Disabilities at 212-998-4980 or mosescsd@nyu.edu. You must be registered with CSD to receive accommodations. Information about the Moses Center can be found at nyu.edu/csd. The Moses Center is located at 726 Broadway on the 2nd floor. Please do this at the start of the semester.