Segmentation: Theory and Algorithms

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Color in Depth: Projections, Reslicing Summary

Covers color models, projections, and reslicing in image analysis for life sciences.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Image Processing I: Segmentation and Thresholding

Explores image segmentation, thresholding techniques, texture segmentation, and connected-component labeling in image processing.

Clustering: Unsupervised Learning

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Color & Color Models

Delves into various color spaces, Color Deconvolution, and analyzing Whole Slide Images.

LED, Light and Color

Covers the energy and radiation of LEDs, transitions in energy levels, and color synthesis.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Image Acquisition

Covers the basics of image acquisition, including optical devices, resolution factors, lens distortions, and sensor technologies.

Deep learning in the IoT context

Delves into deep learning for image and object classification in IoT systems, including clustering techniques and privacy concerns.