Email: Office hours: Wed 11:00 AM - 12:30 PM EST, Zoom link (subject to changes, please see the zoom tab for updates).ĭue to Cornell Tech policies, all office hours will be held remotely on zoom. Email: Office hours: Thurs 11:30 - 1:00 pm EST on Zoom link Email: Office hours: Fri 10.30 am to 12.00pm EST, Zoom link (subject to changes, please see the zoom tab for updates). Volodymyr will be taking questions in the auditorium after the lecture and then move to the tables outside Bloomberg 131. Office Hours: Tue 2:15pm-3:15pm (after class) Thu 2:15pm-2:45pm (after class). Location: in-person: Bloomberg Center 131 online: zoom at. Times: Tues/ Thurs 1:00pm - 2:15pm Eastern Time. Instruction will held be in the hybrid form of online and in-person, zoom link for the online option can be found above. The class will be held twice a week, on Tuesdays and Thursdays. You can also use the following Zoom link to connect to the lecture: The first lecture is going to be on Thursday Aug 26 at 1:00pm - 2:15pm ET at Bloomberg Center 131. PrerequisitesĬS 2800 or equivalent, Linear Algebra, Probability, and experience programming with Python, or permission of the instructor First Lecture Information Any thoughts, comments, advice will be appreciated. Feedback Formĭuring anytime of this course, students can submit their feedback on the course via this link, the form can be submitted for any number of times throughout the semester so that the teaching team could make prompt adjustments. Note also that all videos from last year are available online on Youtube. The lectures videos will be streamed on Zoom until mid-Fall and recordings will be made available on Canvas under the Zoom tab. Course MaterialsĪll the lecture slides and executable notebooks will be posted to our Github repo.
Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, and dimensionality reduction.
Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Learn and apply key concepts of modeling, analysis and validation from machine learning, data mining and signal processing to analyze and extract meaning from data.
0 kommentar(er)
