Lecture

Linear Models & k-NN

Related lectures (41)
Linear Models for Classification
Covers linear models for classification, including SVM, decision boundaries, support vectors, and Lagrange duality.
Unsupervised Learning: Dimensionality Reduction
Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.
Kernel Methods: SVM and Regression
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.
Flexibility of Models & Bias-Variance Trade-Off
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Introduction to Machine Learning: Linear Models
Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.
Multiclass Classification
Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Linear Models for Classification
Explores linear models, logistic regression, classification metrics, SVM, and their practical use in data science methods.
K-Nearest Neighbors & Feature Expansion
Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.
Feature Expansion: Kernels and KNN
Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.
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