Introduction to Machine Learning: Linear Models

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Page 3 of 4

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

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

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.