Probability at Statistical Methods for Data Science

Course content

  • Introduction into concepts of Statistical Learning Theory (Losses, Risk, Consistency, Rates of Convergence, Generalization, Under- and Overfitting, Model Selection)
  • Methods to design Machine Learning Algorithms (Empirical Risk Minimization, Maximum Likelihood Estimation, Bayesian Method) 
  • Regularization Approaches and Algorithms (Linear Model, Ordinary Least Squares, Ridge Regression, Gradient Descent, Lasso, Kernel Density Estimators)

Course information

Code  
Degree programme(s) Mathematics
Lecturer(s) Dr. Yana Kinderknecht
Type of course Lecture and exercise course
Semester Summer semester
Language of instruction English
Level of study Master
ECTS credits  
Contact person Dr. Yana Kinderknecht