Linear Regression: Basics and Gradient Descent

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Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Supervised Learning: Linear Regression

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

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.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Supervised Learning: Classification Algorithms

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

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.