Non Conceptual Knowledge Systems

Related lectures (52)

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Covers Information Extraction approaches, including hand-written patterns and supervised learning.

Covers f-divergences and generalization error in advanced information theory.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Explores matrix factorization for information extraction, Bayesian ranking, and relation embeddings.

Explores Principal Component Analysis for dimension reduction in datasets and its implications for supervised learning algorithms.

Explores binary classification by regression, decision functions, and various cost functions.

Explores algorithms and techniques for information extraction, including Viterbi algorithm, named entities recognition, and distant supervision.

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

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