Linear Models: Part 2

Related lectures (31)

Page 3 of 4

Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.

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

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Explores the extension of linear classifiers to handle multiclass problems and compares their performance on various datasets.

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.