Machine Learning Fundamentals

Related lectures (31)

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

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

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

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

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

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

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

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

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.