Subject Area | Signals, Communications, and Networking |
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Semester | Semester 5 – Fall |
Type | Elective |
Teaching Hours | 4 |
ECTS | 6 |
Prerequisites |
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Course Director |
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Course Instructor |
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Scientific Responsible |
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Title | MLSysOps: Machine Learning for Autonomic System Operation in the Heterogeneous Edge-Cloud Continuum |
Duration | 2023 – 2025 |
Site | https://csl.e-ce.uth.gr/projects/mlsysops |
Department of Electrical and Computer Engineering | |
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Tel. | +30 24210 74967, +30 24210 74934 |
gece ΑΤ e-ce.uth.gr | |
PGS Tel. | +30 24210 74933 |
PGS e-mail | pgsec ΑΤ e-ce.uth.gr |
URL | https://www.e-ce.uth.gr/contact-info/?lang=en |
Subject Area | Signals, Communications, and Networking |
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Semester | Semester 5 – Fall |
Type | Elective |
Teaching Hours | 4 |
ECTS | 6 |
Prerequisites |
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Course Director |
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Course Instructor |
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This course provides an elementary introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
The purpose of this course is to present the necessary statistical techniques for data processing. At the end of this course the student will be able to: describe the statistical properties of data, effectively deals with probability distributions and random variables (discrete and/or continuous), and understands and applies statistical inference in a variety of important applications.