Instructors | Mengye Ren |
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Lecture | Tuesday 4:55pm-6:55pm (31 Washington Pl, Room 411) |
This course covers a wide variety of introductory topics in machine learning and statistical modeling, including statistical learning theory, convex optimization, generative and discriminative models, kernel methods, boosting, latent variable models and so on. The primary goal is to provide students with the tools and principles needed to solve the machine learning problems found in practice. Course syllabus can be found here.
For registration information, please contact CS Graduate Office.
If you'd like to waive the prerequisites, please send an email to the instructor. For each prerequisite, please clearly list which courses you've taken are equivalent, and highlight it in the transcript.
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Late Policy: Homeworks are due at 12:00 PM Eastern time (Noon) on the date specified. You have 4 late days in total which can be used throughout the semester without penalty. Once you run out of late days, each additional late day will incur a 20% penalty. For example, if you submit an assignment 1 day late after using all your late days, a score of 90 will only be counted as 72. Note that the maximum late days per homework is two days, meaning that Gradescope will not accept submissions 48 hours after the due date.
Collaboration Policy: You may form study groups and discuss problems with your classmates. However, you must write up the homework solutions and the code from scratch, without referring to notes from your joint session. In your solution to each problem, you must write down the names of any person with whom you discussed the problem—this will not affect your grade.
Submission: Homework should be submitted through Gradescope. If you have not used Gradescope before, please watch this short video: "For students: submitting homework." At the beginning of the semester, you will be added to the Gradescope class roster. This will give you access to the course page, and the assignment submission form. To submit assignments, you will need to:
Feedback: Check Gradescope to get your scores on each individual problem, as well as comments on your answers. Regrading requests should be submitted on Gradescope.
The final course project constitutes 30% of your overall grade. The objective is to apply the machine learning concepts acquired during this course to a real-world problem. Choose a pertinent and applicable issue, identify an appropriate data source for your machine learning solution, and if no suitable data source exists, propose methods to gather the required data efficiently. More project instruction
Template and detailed project instructions package can be found here: pdf zip
Mengye Ren is an Assistant Professor of Computer Science and Data Science at NYU. His research focuses on deep learning and computer vision.
Pavan is a PhD student in Computer Science at NYU Courant. His research interests center around end-to-end fairness in ML pipelines.
Yilun is a PhD student in Data Science at NYU. His My research interests includes large language models, diffusion models, self-supervised learning, etc.
Yash is a second-year Master student in Computer Science at NYU Tandon Engineering.