Stock Return Prediction

Related lectures (32)

Page 3 of 4

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

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

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

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

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

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

Introduces regularization techniques to prevent overfitting in machine learning models.