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Related lectures (32)

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Biases, ML performance and adversarial ML threats

Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Perception: Image Classification Challenges

Covers image classification challenges, machine learning concepts, linear regression, and nearest neighbor approach in autonomous vehicles.

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.

Open-World Learning for Biomedical Data

Explores open-world learning for biomedical data, focusing on leveraging small datasets and discovering novel phenomena.

Gaussian Discriminant Rule: Classification & Boundaries

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Gaussian Mixture Regression: Theory and Applications

Explores Gaussian mixture regression and overfitting with multiple Gauss functions.

Machine Learning for Physicists/Chemists: Image Classification

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.