Course information provided by the 2026-2027 Catalog.
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. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, and dimensionality reduction.
Prerequisites background equivalent to: CS 2800 and basic familiarity with Matlab or Python, or permission of instructor.
Enrollment Information Enrollment limited to: Cornell Tech students.
Last 4 Terms Offered 2025FA, 2024FA, 2023FA, 2022FA
3 Credits
GradeNoAud(Letter grades only (no audit))
Class Number & Section Details
17867CS 5785 LEC 030
Meeting Pattern
MW
Aug 24 - Dec 7, 2026
Instructors
Gan, K
To be determined. There are currently no textbooks/materials listed, or no textbooks/materials
required, for this section. Additional information may be found on the syllabus provided by your professor.
For the most current information about textbooks, including the timing and options for purchase, see the
Cornell Store.
Additional Information
Instruction Mode: In Person
Syllabi:
none
Regular Academic Session.
Credits and Grading Basis
3 Credits
GradeNoAud(Letter grades only (no audit))
Class Number & Section Details
17868CS 5785 LEC 031
Meeting Pattern
MW
Aug 24 - Dec 7, 2026
Instructors
Blumtritt, J
Sekhari, A
To be determined. There are currently no textbooks/materials listed, or no textbooks/materials
required, for this section. Additional information may be found on the syllabus provided by your professor.
For the most current information about textbooks, including the timing and options for purchase, see the
Cornell Store.
