Unsupervised Learning in Computer Vision

Research Computing Center

John Crerar Library - Kathleen A. Zar Room

Unsupervised learning methods are playing an&nbsp; increasingly vital role in&nbsp; the field of computer vision enabling algorithms to discover patterns in data without being given predefined categories or target outputs. These techniques are driving breakthroughs in areas such as image segmentation,&nbsp; dimensionality reduction, and anomaly detection.&nbsp;&nbsp; The workshop will introduce participants to unsupervised learning techniques and their applications in computer vision, with a focus on anomaly detection. Through hands-on exercises, participants will gain practical experience applying these methods to detect anomalies in medical images using PyTorch.&nbsp; Objectives: <ul><li>Understand the fundamentals of unsupervised learning in computer vision applications.</li></ul> <ul><li>Explore anomaly detection, a key skill for identifying outliers, uncovering fraud, correcting&nbsp; data errors, and&nbsp; recognizing unusual patterns.&nbsp;</li></ul> <ul><li>Implement unsupervised anomaly detection in medical images through hands-on exercises using PyTorch.&nbsp;</li></ul> Level: IntermediateDuration: 2 hoursPrerequisites: A basic understanding of machine learning concepts as well as experience with Python programming are essential. Experience with a machine learning library, such as PyTorch is required. Participants are encouraged to bring their laptops for an engaging and immersive learning experience. An RCC account is optional.

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Unsupervised Learning in Computer Vision

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Unsupervised Learning in Computer Vision

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