Lectures related to Binary Classification Cost Function

Binary Classification by Regression: Decision Functions and Cost Functions

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

Supervised Learning: Image Space and Labeling

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

Linear Models for Classification: Multi-Class Extensions

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

Supervised Learning: Formalization and Cost Functions

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

Logistic Regression: Probability Modeling

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

Supervised Learning in Asset Pricing

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

Supervised Learning: Classification Algorithms

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

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.

Classification: Decision Trees and kNN

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.

Linear Regression: Basics

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

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 Regression and Logistic Regression

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

Gradient Descent: MNIST Dataset and Logistic Loss

Focuses on implementing gradient descent with the MNIST dataset and logistic loss in machine learning.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Linear Models for Classification: Logistic Regression and SVM

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

Decision Trees: Classification

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

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

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