Recommender Systems: Text Classification & Naïve Bayes

Related lectures (29)

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Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.

Introduces the basics of information retrieval, covering text-based and Boolean retrieval, vector space retrieval, and similarity computation.

Covers learning bounds, complexities, growth function, shattering, and VC dimension in binary classifiers.

Covers exercises on statistical modeling, including Gibbs Ising, GCM pruning, fairness, and simplification in teacher-student models.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.