Introduction to Machine Learning

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Related lectures (31)

Page 3 of 4

Improving Models of the Ventral Visual Pathway

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Classification: Introduction

Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.

Neural Networks: Supervised Learning and Backpropagation

Explains neural networks, supervised learning, and backpropagation for training and improving performance.

Image Representation Insights

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

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.

Introduction to Supervised Learning

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

Machine Learning Biases

Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.

Image Processing II: Bayesian Classification and Decision Making

Explores Bayesian classification, decision making, and pattern recognition applications in image processing.