Unsupervised learning

Related lectures (30)

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

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

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

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.