Logistic Regression: Cost Functions & Optimization

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Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Gradient Descent: Linear Regression

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Machine Learning Review

Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.

Probability and Statistics: Basics and Applications

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Linear Models: Part 2

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