Lectures related to Binary Classification Cost Function

Generalized Linear Regression: Classification

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

Linear Classification: Parameterizing Lines and Distance Calculation

Covers the parameterization of lines in 2D space and the perceptron algorithm.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Classification with GMM

Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Linear Binary Classification: Perceptron, SGD, Fisher's LDA

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

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Neural Network Training

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Binary Classification MOOC: IoT Systems and Industrial Applications with Design Thinking

Explains how to find the best hyperplane to separate two classes.

Binary Classification by Regression: Decision Functions and Cost Functions Log in to Mediaspace to watch this video

Explores binary classification by regression, decision functions, and various cost functions.

Supervised Learning: Image Space and Labeling Log in to Mediaspace to watch this video

Covers supervised learning, binary and multi-class classification problems, and making predictions from labeled examples.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Supervised Learning: Formalization and Cost Functions Log in to Mediaspace to watch this video

Covers the formalism for supervised learning and decision functions in classification problems.

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

Covers logistic regression for binary classification using probability modeling and optimization methods.

Supervised Learning in Asset Pricing Log in to Mediaspace to watch this video

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

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

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

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

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.