Lecture 2 – Regression and its interpretation#

Contents of this lecture

Many of you may have already studied the basics of regression and fitting. In this part, we will explain how mathematically the regression can be interpreated as machine learning methods and to be connected with more advanced learning algorithms.

  • file: contents/P2_regression/lecture2-interperations.md title: Lecture 2 – Regression and interpretation sections:

    • file: contents/P2_regression/tutorials/lecture2/examples.ipynb title: Computer Examples

    • file: contents/P2_regression/tutorials/lecture2/excercise.ipynb title: Computer Excercises

  • file: contents/P2_regression/lecture3-poly-spline.md title: Lecture 3 – Polynominal and spline fitting
    sections:

    • file: contents/P2_regression/tutorials/lecture3/examples.ipynb title: Computer Examples

    • file: contents/P2_regression/tutorials/lecture3/excercise.ipynb title: Computer Excercises

  • file: contents/P2_regression/lecture4-reg-gradient.md title: Lecture 4 – Model parameter estimation by gradient
    sections:

    • file: contents/P2_regression/tutorials/lecture4/examples.ipynb title: Computer Examples

    • file: contents/P2_regression/tutorials/lecture4/excercise.ipynb title: Computer Excercises

  • file: contents/P2_regression/lecture5-gam-mem.md title: Lecture 5 – GLM GAM and Mixed-effects model sections:

    • file: contents/P2_regression/tutorials/lecture5/examples.ipynb title: Computer Examples

    • file: contents/P2_regression/tutorials/lecture5/excercise.ipynb title: Computer Excercises