CSYE 7105 — High-Performance Parallel Machine Learning and AI
4 semester hoursGraduateLectureusually offered: fall, springtypical days: T/FBostonTraditional
Explores the parallelization of machine learning and deep learning code that leads to high performance on heterogeneous cluster architectures. Includes the applications to a variety of domains, including image classification, speech recognition, and natural language processing, etc. Covers a brief overview of the emerging parallel computing applications. Analyzes system architectures for different kinds of parallel computing systems (shared-memory system, distributed-memory system, accelerator system, and hybrid). Offers students an opportunity to practice the principles and the practice of the emerging parallelism-based machine-learning paradigm.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 1 | 37 | 50 | 74% | 13.0 |
| Spring 2024 | 1 | 48 | 50 | 96% | 2.0 |
| Fall 2024 | 1 | 44 | 50 | 88% | 6.0 |
| Spring 2025 | 1 | 47 | 50 | 94% | 3.0 |
| Fall 2025 | 1 | 46 | 50 | 92% | 4.0 |
| Spring 2026 | 1 | 38 | 50 | 76% | 12.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 100% · W 0% · Th 0% · F 82%
Common patterns: TF (82% of sections), T (18% of sections) — in patterns, R means Thursday
Professors
Fall
- Handan Liu (100% of students) · reviews
Spring
- Handan Liu (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (CSYE course descriptions) · Student reviews on RateMyHusky · All CSYE courses · Plan it at numap.app