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Linear Models for Classification

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Related lectures (35)

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

Covers linear models for classification, focusing on logistic regression and support vector machines.

Linear and Logistic Regression Log in to Mediaspace to watch this video

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

Linear Regression and Logistic Regression Log in to Mediaspace to watch this video

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Logistic Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Linear Models: Continued Log in to Mediaspace to watch this video

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Linear Models: Part 1 Log in to Mediaspace to watch this video

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Logistic Regression: Cost Functions & Optimization Log in to Mediaspace to watch this video

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

Linear Models: Basics Log in to Mediaspace to watch this video

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Supervised Learning Essentials Log in to Mediaspace to watch this video

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Linear Models: Continued Log in to Mediaspace to watch this video

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

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Supervised Learning: Likelihood Maximization Log in to Mediaspace to watch this video

Covers supervised learning through likelihood maximization to find optimal parameters.

Logistic Regression: Probability Mapping Log in to Mediaspace to watch this video

Explains logistic regression, mapping predictions to probabilities and optimizing likelihood.

Linear Programming Basics Log in to Mediaspace to watch this video

Introduces linear programming basics, including optimization problems, cost functions, simplex algorithm, geometry of linear programs, extreme points, and degeneracy.

Photonic Extreme Learning Machines: Reservoir Computing Techniques Log in to Mediaspace to watch this video

Covers photonic extreme learning machines and reservoir computing, focusing on their architectures, programming techniques, and applications in optical computing.

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