Category
Topics in machine learning
Related lectures (401)
Classification Algorithms: Generative and Discriminative Approaches
Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Linear Binary Classification: Perceptron, SGD, Fisher's LDA
Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Nearest Neighbor Rules: Part 2
Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.
SVM - Principle: Linear Classifiers
Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.
Linear Models for Classification: Part 3
Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.
Support Vector Machines
Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
Max-Margin Classifiers
Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.
Perceptron: Part 2
Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.
Statistical Inference and Machine Learning
Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.