Concept
Naive Bayes classifier
Related lectures (101)
Multiclass SVM
Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.
Unsupervised Learning: PCA & K-means
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
SVM and Multiclass Classification
Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.
Naive Bayes: Gaussian Discriminant Analysis
Covers the Naive Bayes assumption, Gaussian Discriminant Analysis, ML estimates, and Kernel trick.
Naive Bayes Classifier
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Ensemble Methods: Random Forests
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Generalization Theory
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Max-Margin Classifiers
Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.
Document Classification: Overview
Explores document classification methods, including k-Nearest-Neighbors, Naïve Bayes Classifier, transformer models, and multi-head attention.
Nearest Neighbor Rules: Part 2
Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.