This course is asynchronous, which means that we do not have scheduled course meetings except for the midterm and final exams.
The course is organized into modules. At the beginning of each week, materials for the week’s module (including video lessons, slides/notes, and Python notebooks) will be posted on NYU Brightspace. You will work through this material during the week, at whatever time is convenient for you. At the end of the week, you’ll be assigned some homework related to that week’s module, which you’ll submit online.
The midterm and final exams will be synchronous, and will be held during the week indicated in the course syllabus. Several time slots will be offered during this week, so that you can select the one that does not conflict with your work or course schedule. The exams will be held in person for most students, subject to the following:
This is a challenging, fast-paced course, and includes quite a bit of material you will need to work through each week. But, you’re not alone - I am always happy to answer questions, clarify any points of uncertainty, or explain anything that needs extra explanation.
There are two main ways to get help:
(Note that you can ask questions anonymously on the Q&A forum, if you feel more comfortable that way.)
Your mastery of the course material will be evaluated by your performance on:
The relative weight of each of these components will be shared via the course syllabus in the first week of the semester.
This is a 3-credit course. During a fall or spring semester, the average student should spend at least 3 hours/week/credit → 9 hours/week for a 3 credit course.
A typical week will include the following:
A student who is not very comfortable with the prerequisites may have to spend more time than the estimated 9 hours/week in order to do well.
This course is mathematically oriented, and undergraduate-level knowledge of probability and linear algebra is required.
If you want to brush up, you can review:
There will be a significant programming component to this course, and class and homework exercises will be in Python. You do not need to know Python a priori, but you should know programming concepts and have experience programming in some programming language, and you should be comfortable picking up Python basics independently. We will review some important Python basics in the first week of the course.
This is an introductory graduate level course and no prior machine learning knowledge will be assumed. If you already have significant ML experience, there is no need to take this class.
In this course, we will use the Google Colab environment for practical programming demos and exercises. Colab is a free browser-based environment for Python programming. You don’t need to install anything to use Colab - you’ll just need a browser.
You won’t need to buy a textbook - all of the materials you’ll need will be posted on the course site.
Here is a rough outline of the course content (subject to change):
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 both in and out of class, 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.
If you are student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities (CSD) 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 www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 3rd floor.
Note for those who require accommodations for timed assessments: the final exam will be a timed exam.
If you are experiencing an illness or any other situation that might affect your academic performance in a class, please contact the student advocate: eng.studentadvocate@nyu.edu. The student advocate can reach out to your instructors to make arrangements on your behalf, when warranted.