Syllabus Application
Special Topics in CS: Scalable Learning Systems
CS 58010
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Special Topics in CS: Scalable Learning Systems - CS 58010
Classroom:
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Level of course:
Masters
Course Credits:
SU Credit:3.000, ECTS:10
Prerequisites:
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Corequisites:
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Course Type:
Lecture
Instructor(s) Information
Kubilay Atasu
Course Information
Catalog Course Description
This course provides a broad overview of ofstate-of-the-art parallel and distributed machine learning (ML) and deep learning (DL) algorithms and systems, with a strong focus on the scalability, resource efficiency, data requirements, and robustness of the solutions. This course covers effective ways to map state-of-the-art ML and DL solutions to parallel AI accelerators such as GPUs and TPUs. A set of techniques are presented to efficiently scale ML and DL workloads to a large number of distributed machines in the presence of system failures and malicious attacks. Finally, methods for improving the scalability and efficiency of generative learning and graph learning approaches are covered.
Course Learning Outcomes:
| 1. | Demonstrate deep understanding of parallel and distributed machine learning and deep learning algorithms and systems by analyzing and discussing research papers. |
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| 2. | Design and implement scalable and efficient machine and deep learning algorithms and systems, evaluate time-space and cost-performance tradeoffs. |
| 3. | Analyze and evaluate the resiliency of federated machine learning algorithms and systems against various attacks. |
| 4. | Formulate research questions and objectives and relate them to the relevant literature in the broad field of deep generative learning systems. |
Course Objective
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Course Materials
Resources:
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Technology Requirements:
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