Topics in machine learning

Related lectures (29)

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

Covers the Perceptron model, SGD, and Fisher's Linear Discriminant in binary classification.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.

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

Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.