Teaching performance analysis: essential skills andlearning outcomes

被引：0

作者：

Fiems, Dieter ^{[1
]}

机构：

[1] Ghent University, Department TELIN, St-Pietersnieuwstraat 41, Gent

来源：

Performance Evaluation Review | 2024年 / 52卷 / 02期

关键词：

Adversarial machine learning - Contrastive Learning - Curricula - Federated learning - Stochastic systems - Students - Teaching;

D O I：

10.1145/3695411.3695430

中图分类号：

学科分类号：

摘要：

In the age of machine learning, traditional performance analysis courses face challenges such as declining student interest and increasing competition from courses within the respective study programmes. At the same time, courses must accommodate increasingly heterogeneous groups of students, both in terms of background, interests and mathematical ability. In this paper, we present a personal perspective on teaching performance evaluation techniques. We argue that stochastic modelling should be the focus in a performance analysis course and that stochastic analysis techniques are a means to an end to solve performance problems, not the main focus. © 2024 Copyright is held by the owner/author(s).

引用

页码：49 / 52

页数：3

共 8 条

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