Machine Learning Phases

Related lectures (32)

Page 3 of 4

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Covers the basics of machine learning, including dataset division and model training.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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

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

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.