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Unit of study_
ELEC3612: Pattern Recognition and Machine Intelligence
2025 unit information
This unit provides a hands-on pattern recognition and machine learning course, towards solving the practical problems in computer vision and signal processing. The content of the unit is organized in a task-oriented way, including feature extraction and selection, classification, regression, outlier detection, sparse representation and dictionary learning, etc. The fundamentals of pattern recognition algorithms, such as PCA, LDA, support vector machine, ensemble, random forest, kernel methods, graphical models, etc., are delivered in the context of computer vision (such as image and video) and signal processing (such as audio, optical, and wireless signals) applications. In addition to mathematical foundations, this unit gives the students hands-on training about how to program these algorithms using python packages.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Undergraduate |
|---|---|
| Academic unit | School of Electrical and Computer Engineering |
| Credit points | 6 |
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Prerequisites:
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[(MATH1X61 or MATH1971) or MATH1X02] and [(MATH1X62 or MATH1972) or (MATH1X05 or BUSS1020)] |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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1st year mathematics and 1st year Software Engineering/Electrical Engineering. Linear Algebra, Basic Programming skill |
At the completion of this unit, you should be able to:
- LO1. Understand the principles, algorithms, and model evaluation in pattern recognition and machine learning
- LO2. Apply pattern recognition and machine learning methods to solving the practical problems in computer vision and signal processing
- LO3. Master python programming for pattern recognition and gain hands-on experience
- LO4. Learn to report results in professional manner
- LO5. Develop some basic teamwork and project management skills through a group project
Unit availability
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The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
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Modes of attendance (MoA)
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