Description
Basic linear algebra operations and their use in approximations underlying machine learning, with applications to solving regression, classification, and clustering problems.
Students completing the course will be able to:
· Utilize linear algebra to represent system behavior.
· Fit a linear algebra model to real-world data and their use in deriving optima.
· Utilize linear algebra for dimensionality reduction and to evaluate the quality of a regression, classification, or clustering model.
Instructors
Prerequisites
Basic Enrollment Recommendations: Familiarity with differential, integral, and multivariate calculus, and basic linear algebra. Knowledge of Python is beneficial. At Tufts, this may be Calculus (e.g., Math 32, 34, and 42) and linear algebra (e.g., Math 70 or 72).
This course will be held:
Dates: January 3-13, 2023
Format: online, with synchronous daily sessions from 6-7:30pm ET
Cost: $1697 (limited funding available for PhD candidates, please reach out to courses@tufts.edu for information)
How to enroll:
Tufts Degree Students (graduate and PhD)
Please follow the instructions below to enroll in a winter term for-credit course.
- Email Course Updates at course_updates@tufts.edu and request to be term activated for the Winter Session. You will receive an email back within two business days confirming your activation.
- Once you have been activated, log in to SIS and enroll in a winter term course.
Non-Degree Students (visiting students and working professionals)
Register via University College’s website by following the link