Overview
Information Systems : Practical knowledge of issues involving learning with large datasets. Topic include: data governance and management at scale; principles of feature engineering, parallel and distributed computing for machine learning; techniques for scalable supervised and unsupervised learning; analysis of programs in terms of memory, computation, and (for parallel methods) communication complexity; and methods for low-latency inference.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Prerequisites: (INSY 660 or INSY 662) and (MGSC 660 or MGSC 661) or permission of the instructor
Minimum Grade or Test Scores : B-
Restrictions: Not open to students who have taken INSY 695 when the topic was "Enterprise Data Science & Machine Learning in Production 1"
The online version of the course includes synchronous and/or asynchronous course activities
