Logistic Regression: New Formulation and Loss Function

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 2 of 4

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Convex Optimization: Examples of Convex Functions

Explores convex optimization, convex functions, and their properties, including strict convexity and strong convexity, as well as different types of convex functions like linear affine functions and norms.

Optimization Basics

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Linear Models for Classification

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Expectation Maximization: Learning Parameters

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Logistic Regression: Classification

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Supervised Learning: Linear Regression

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Linear Models: Part 2

Covers linear models, binary and multi-class classification, and logistic regression with practical examples.