Lectures

• 5.2.1 How to use Linear Models for Nonlinear Tasks?

  Subject(s) Module 05. Overfitting
  

• 5.2.2 How Flexible? Bias Variance Decomposition, Underfitting, Overfitting

  Subject(s) Module 05. Overfitting
  

• 5.2.3 Complexity Curves, Features Curves, how to Tune Complexity?

  Subject(s) Module 05. Overfitting
  

• 5.2.4 Learning Curve: How Much Data do we Need?

  Subject(s) Module 05. Overfitting
  

• 6.2.1 Cross-Validation

  Subject(s) Module 06. Cross Validation & Regularization
  

• 6.2.2 Cross Validation & Hyperparameter Tuning/ Model Selection

  Subject(s) Module 06. Cross Validation & Regularization
  

• 6.3.1 Improving the Linear Model

  Subject(s) Module 06. Cross Validation & Regularization
  

• 6.3.2 Ridge Regression

  Subject(s) Module 06. Cross Validation & Regularization
  

• 6.3.3 Lasso

  Subject(s) Module 06. Cross Validation & Regularization
  

• 6.3.4 Ridge Regression versus Lasso

  Subject(s) Module 06. Cross Validation & Regularization
  

AI skills for Engineers: Supervised Machine Learning by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://online-learning.tudelft.nl/courses/ai-skills-for-engineers-supervised-machine-learning/