Statistical Modeling

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Related lectures (29)

Page 3 of 3

Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.

Link Prediction: Missing Edges and Probabilistic Methods

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Document Classification: Features and Models

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Document Classification

Explores document classification methods, including Naïve Bayes and word embeddings.

Bayesian Inference: Gaussian Variables

Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

K-Nearest Neighbors & Feature Expansion

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

Regularized Cross-Entropy Risk

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

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