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Lecture
Classification with GMM
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Related lectures (51)
Elements of Statistics: Probability, Distributions, and Estimation
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Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Extreme Value Theory: Statistical Applications
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Explores statistical applications of threshold exceedances and GPD modeling in extreme value theory.
Wireless Receivers: Parameter Estimation
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Covers the challenges and techniques of parameter estimation in wireless receivers.
Nearest Neighbor Classifiers and Curse of Dimensionality
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Explores nearest neighbor classifiers, bias-variance tradeoff, curse of dimensionality, and generalization bounds in supervised machine learning.
Factor Models: Latent Variables and Asset Pricing
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Covers Factor Models, including PCA, asset pricing, factor investing, GMM, and Fama-McBeth estimation.
Decision Trees and Random Forests: Concepts and Applications
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Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.
Untitled
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The Stein Phenomenon and Superefficiency
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Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
Logistic Regression: Classification
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Covers supervised learning, classification using logistic regression, and challenges in optimization.
Design of Experiments: Response Surface
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Explores experimental design methodology, including classic plans, simplex method, and canonical analysis for linear and quadratic models.
Linear Models: Part 2
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Covers linear models, binary and multi-class classification, and logistic regression with practical examples.
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