Table of contents
When/Where
Lecture and Sections
- Lecture: Fridays 10:15am-12:15pm, 19 West 4th, Room 101
- Section: Wednesdays 7:10pm-8:00pm, 19 West 4th, Room 101
Office hours
We will have five office hours each week: one with the instructor (lecture or general questions) and one with each of the four section leaders (lecture or assignment questions). You are also encouraged to ask questions on Discord, which will be answered by the TAs or your classmates. See staff page for office hour details.
Course Policy
Communication
All key announcements will be made in Brightspace. We will use Discord as a communication tool for answering questions related to the lectures, assignments, and projects. The registration link for Discord will be available on Brightspace. All assignment / quiz submissions should be made in GradeScope. If you’d like to message course staff privately, please email to fa25-dsga1011-staff@googlegroups.com. The instructor / TAs will have access to this email.
Prerequisites
Students are expected to have a solid mathematics background and strong programming skills.
- Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235)
- Algorithms and data structure (CSCI-UA.102)
- Basic knowledge in machine learning (DS-GA.1003, CSCI-UA.0473). We will not spend a significant amount of time on machine learning basics so some prior exposure to the supervised learning framework (e.g., loss functions, SGD) is expected.
Late-day Policy
Each student will have a total of seven (7) late days throughout the semester that you can use for the assignment portion and proposal & intermediate report of the final project. You do not need to ask for permissions to use them, but the course staff will keep track of how many days were used. If you exceed the total number of late days, then your assignment will not receive full credit (max 80% of total grade after 1 late day, 60% of total possible grade after 2 late days, 40% of the total possible grade after 3 late days, 0% afterwards). You cannot use late days for other elements of the course besides assignments (i.e., final project poster presentation / final report or quiz). This budget should cover most emergencies and personal circumstances; I will not approve further extensions, so don’t use up all of your budget on the first assignment deadline. If the late date is used in the team assignment, it’ll use late day for every member of the team.
Collaboration policy
You may discuss problems with your classmates. However, you must write up the homework solutions and the code yourself. In the submission, you must write down the names of any person with whom you discussed the problem; this will not affect your grade.
Academic Integrity
(Adapted from the website of the College of Arts & Science) Academic honesty means that the work you submit — in whatever form — is original. Obviously, bringing answers into an examination or copying all or part of a paper straight from a book, the Internet, or a fellow student is a violation of this principle. But there are other forms of cheating or plagiarizing which are just as serious — for example, presenting an oral report drawn without attribution from other sources (oral or written), writing a paragraph which, despite being in different words, expresses someone else’s idea without a reference to the source of the idea, or submitting essentially the same paper in two different courses (unless both instructors have given their permission in advance).
Using Artificial Intelligence
The use of artificial intelligence tools (such as ChatGPT, Cursor, Github Copilot) in this class is permitted, but you have to describe (1) how you used them in a few sentences (your major goals with using AI assistance and prompt/AI model output samples) and (2) cite the contents generated by those tools appropriately each time you use them. If you are considering the use of AI tools but are unsure if you are allowed or the extent to which they may be used, please ask the course staff.
Academic Accommodations
Academic accommodations are available for students with disabilities. The Moses Center website. Please contact the Moses Center for Student Accessibility (212-998-4980 or mosescsd@nyu.edu) for further information. Students who are requesting academic accommodations are advised to reach out to the Moses Center as early as possible in the semester for assistance.
Student Wellness
In a large, complex community like NYU, it’s vital to reach out to others, particularly those who are isolated or engaged in self-destructive activities. Student wellness is the responsibility of all of us. The NYU Wellness Exchange is the constellation of NYU’s programs and services designed to address the overall health and mental health needs of its students. Students can access this service 24 hours a day, seven days a week - wellness.exchange@nyu.edu; (212) 443-9999. Students can call the Wellness Exchange hotline (212-443-9999) or the NYU Counseling Service (212-998-4780) to make an appointment for Single Session, Short-term, or Group counseling sessions.
CDS Community Member Statement
Please read this document carefully, which lays out the expectations of the CDS community. CDS Community Member Guidelines and Expectations.
Coursework Overview
There will be three components that will count towards your grade. The deadlines can be found in course schedule.
- Assignments (35%): There will be four assignments, each counting 10% of the final grade.
- In-class Quiz (25%): There will be four closed-book in-class online quizzes. We will drop the lowest quiz grade, and the rest will count equally towards the final grade. You are expected to attend each lecture, and if you miss the quiz then you will get 0% for that quiz portion. If you have to be absent for more than one quiz day for valid reasons (i.e., religious observance, documented illness (please don’t show up sick), family emergency, and others), please reach out to the instruction team (fa25-dsga1011-staff@googlegroups.com) as soon as possible, it’ll be handled on a case-by-case basis.
- Final Project (40%): You are required to complete a (group) project applying techniques learned in this course. All group members will receive the same grade.
Submission: Assignments are submitted through Gradescope. At the beginning of the semester, you will be added to the Gradescope roster through Brightspace. Please do not register on Gradescope separately or change your email; this will cause the rosters to be out-of-sync.
Grading: We aim to release grades within two weeks of the submission date. Once the grades are released, you will have one week to submit any regrading requests. If the late date is used in the team assignment, it’ll use late day for every member of the team.
Assignment
There will be a total of 4 programming assignments.
- Assignment 1: NN basics / classification models
- Assignment 2: Transformer from scratch
- Assignment 3: Prompting LLMs
- Assignment 4: Fine-tuning LLMs
In-Class Quiz
There will be a total of 4 quizzes. Each quiz will happen during the first 15 minutes of the class. The quiz will consist of a few questions: True/False, multiple choice and short answer questions that evaluate the concepts covered during the lecture in the weeks preceding the quiz. At least one week prior to the first quiz, the course staff will post some sample quiz questions to help your preparation. We will drop the lowest quiz grade when computing the final score, and the other three quizzes will count equally towards your final grade.
Final Project
Please see this document for instructions on the final project.
Final Grade
Your letter grade will be assigned based on the following schema (i.e., you should achieve a final grade equal or higher than the cutoff listed below for each letter grade).
| Letter Grade | Cutoff |
|---|---|
| A | 93 |
| A- | 90 |
| B+ | 87 |
| B | 83 |
| B- | 80 |
| C+ | 77 |
| C | 73 |
| C- | 70 |
| D | 65 |
| F | Below 65 |