Metrics for Classification

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Related lectures (32)

Model Complexity and Overfitting in Machine Learning Log in to Mediaspace to watch this video

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Data Representations and Processing Log in to Mediaspace to watch this video

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Graph Coloring: Theory and Applications Log in to Mediaspace to watch this video

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Markov Chain Monte Carlo: Sampling and Convergence Log in to Mediaspace to watch this video

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Sampling: Signal Reconstruction and Aliasing Log in to Mediaspace to watch this video

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Polynomial Regression: Basics and Regularization Log in to Mediaspace to watch this video

Covers the basics of polynomial regression and regularization to prevent overfitting.

Signals & Systems I: Sampling and Reconstruction Log in to Mediaspace to watch this video

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Machine Learning Review Log in to Mediaspace to watch this video

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Sampling: DT-time processing of CT signals Log in to Mediaspace to watch this video

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Introduction to Sampling Log in to Mediaspace to watch this video

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

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