Image Processing II: Bayesian Classification and Decision Making

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Introduction to Supervised Learning

Introduces supervised learning using labeled data points to optimize classifier output.

Introduction to Machine Learning

Introduces fundamental machine learning concepts and algorithms for model training and evaluation.

Deep Learning: Edge Detection and Neural Networks

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Statistical Physics of Learning

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Machine Learning Fundamentals

Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Image Representation Insights

Explores the evolution of image representation, challenges in supervised learning, benefits of self-supervised learning, and recent advancements in SSL.