NYU | Tandon School of Engineering
Integrated Design & Media

DM-GY 9103 | Special Topics in Digital Media
Spring 2024, Section R

Important Dates (for this course)

Course Description

This is a 14-week course meant to introduce students to the wild and wonderful world of data analysis and machine learning from critical, practical and creative perspectives. Through readings, projects and programming assignments, students will develop a solid understanding of Machine Learning applications and techniques related to media processing, analysis and creation. We will use Python and Jupyter notebooks to run, fine-tune and analyze existing machine learning models.


Introduction to Creative Coding (DM-GY 6063 / DM-UY 1133)

Course Objectives

By the end of the course, students will have:

Programming Assignments

Biweekly exercises to experiment with the Python language and libraries for working with data.

Reading Assignments

Four readings from books about the social implications of machine learning and big data systems.

Two readings of classic ML papers.


Around week 10 we will start working on a longer project that will combine many of the topics covered in the class like: data collection and processing, model selection, training and evaluation.

The project will be broken up into milestones for different aspects of its development.


The grade for this course will be determined according to the following:

Activity % of Final Grade
Programming Assignments 50%
Readings 20%
Final Project 30%

Letter grades for the entire course will be assigned as follows:

Letter Grade Points Overall Percent
A 4.00 90% - 100%
B+ 3.33 85% - 89.99%
B 3.00 80% - 84.99%
C+ 2.33 75% - 79.99%
C 2.00 70% - 74.99%
D+ 1.33 65% - 69.99%
D 1.00 60% - 64.99%
F 0.00 0% - 59.99%

Course Schedule (subject to change)

Week 01 (2024/01/25)

Week 02 (2024/02/01)

Week 03 (2024/02/08)

Week 04 (2024/02/15)

Week 05 (2024/02/22)

Week 06 (2024/02/29)

Week 07 (2024/03/07)

Week 08 (2024/03/14)

Week 09 (2024/03/28)

Week 10 (2024/04/04)

Week 11 (2024/04/11)

Week 12 (2024/04/18)

Week 13 (2024/04/25)

Week 14 (2024/05/02)

Week 15 (2024/05/09)

Expectations for Work Outside the Classroom

Students should expect to spend roughly 6 hours each week on supplemental work in this course. This may include reading assignments, homework assignments, writing assignments, research, studying, etc.

Required Materials


All of these are available digitally from the NYU library:

Additional reading materials will be distributed throughout the semester.


Course Policies

Social Etiquette

These Social Rules from The Recurse Center should be kept in mind during class discussions, presentations, critiques:

Also, these observations from a discussion about ethics in tech are a helpful reminder:

“There is a general rule for comedy and art: always punch up, never punch down. We let comedians and artists and miscellaneous jesters do outrageous things as long as they obey this rule. You can poke fun at yourself, you can make a joke at the expense of someone with higher social status than you, otherwise, it’s not cool.”

“If you make a joke, and people get really offended, it’s almost certainly because you violated this rule. People don’t get offended randomly. Explaining that ‘it was just a joke’ doesn’t help; everyone knows what a joke is. The problem is that you used a joke as a means of being an asshole.”

Be sensitive to what your classmates might find offensive, triggering or violent and be graceful and listen carefully when your work gets called out for being offensive, triggering or violent.

Academic Integrity

Violations of academic integrity are considered to be acts of academic dishonesty and include (but are not limited to) cheating, plagiarizing, fabrication, denying other access to information or material and facilitating academic dishonesty, and are subject to the policies and procedures noted in the Student Handbook and within the Course Catalog, including the Student Code of Conduct and the Student Judicial System. Please note that lack of knowledge of citations procedures, for example, is an unacceptable explanation for plagiarism, as is having studied together to produce remarkable similar papers or creative works submitted separately by two students, or recycling work from a previous class.

Please review NYU’s School of Engineering’s academic dishonesty policy in its entirety. Procedures may include, but are not limited to: failing the assignment, failing the course, going in front of an academic judicial council and possible suspension from school. Violations will not be tolerated.

All work for this class must be our own and specific to this semester. Any work recycled from other classes or from another, non-original source will be rejected with serious implications for the student. Plagiarism, knowingly representing the words or ideas of another as one’s own work in any academic exercise, is absolutely unacceptable. Any student who commits plagiarism must re-do the assignment for a grade no higher than a D. In fact, a D is the highest possible course grade for any student who commits plagiarism.

A Special Note on Open Source and “Found Code”

There’s an amount of sharing and re-using that will happen in this course due to the open source nature of the libraries, tools and learning materials we will be using. Plus a lot of assignments will be turned in using github, a platform for sharing code and other content.

Nonetheless, we have to be careful and conscious about the difference between using available tools that help with our learning experience and submitting other people’s work as our own. It’s not hard to find code online that will do things similar to, or exactly alike, the things you will be developing for this class. It is NOT ok to use those as part of an assignment or project for this class.

On the other hand, it’s also not hard to find code/libraries/packages/examples that solve specific technical tasks that are part of a larger project or assignment. For example: a library that converts gifs into movies or code for working with different text encodings. These are specific tasks that we aren’t going to solve on our own, so using an open source solution is acceptable and expected.

You are expected to cite any tutorials, examples, libraries or inspiration that you use for our work. Sometimes the authors have a specific way they’d like their work cited (CC licenses), otherwise a name and a link is fine.

TL;DR: Copying assignment code from other sources, using any code from other sources with only slight modifications or using any code from other sources without a reference is plagiarism.

If there are questions about a specific situation, please ask.

A Special Note on LLMs and Large Diffusion Models

This is a class about creative uses of technology, and LLMs and interfaces like ChatGPT, Midjourney, etc definitely have their place in discussions about technology, society and creativity. Being in the environment we are in, it has become impossible to ignore these tools.

Having said that….

You may use AI interfaces to help generate ideas and images as it pertains to the brainstorming or ideation portions of a project and/or to generate images for presentations, as long as proper credit is given.

You may NOT submit any work generated by these interfaces as your own.

Given that this is an introductory course about ML, and we’re all starting fresh, I don’t believe there is any situation that will warrant the use of these tools for generating code. Save that for a future when you might have to write tedious software for parsing/flipping/transforming database entries. There should be an aura of fun to the code you are writing, and a sense of accomplishment in learning how to make computers do unexpected things, that doesn’t justify the use of tools that generate code.

As always, if there are any questions about a specific situation, just ask.

Academic Accommodations

If you are a 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 https://www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 2nd floor.

If you are experiencing an illness or any other situation that might affect your academic performance in a class, please email the Office of Advocacy, Compliance and Student Affairs: eng.studentadvocate@nyu.edu.


The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs, ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations, and abilities will be treated with respect. It is my intent that all students’ learning needs be addressed, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. If this standard is not being upheld, please feel free to speak with me.