Plans for week 36

Dear all, welcome to a new week.  Project 1 is now available and we will discuss this both during the lab sessions and during the various lectures. You can find the project either by scrolling down to project 1 at https://compphysics.github.io/MachineLearning/doc/web/course.html (you will find the project as a latex file, a pdf file, a jupyter-notebook and various html style). Let us know if you spot typos and/or inconsistencies.  You can also obtain all new files by a git pull or going to the folder  https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2022/Project1.

This week at the lab you can opt to start with the project or do the exercises from last week (which prepare your for project 1). Feel free to opt for the alternative which fits best. 

Else, this week we continue our discussion of the singular value decomposition in connection with inverses of matrices, Ridge and Lasso regression and interpretations of these. Thereafter we go back to a statistical interpretation of these methods and link their analysis and results with a Bayesian analysis.  We will also discuss during the lectures various technicalities connected with preparing our data and obviously project 1. 

The lecture material this week can be found both in the slides from week 35 and week 36, see again the link https://compphysics.github.io/MachineLearning/doc/web/course.html. You can also read about these methods at the jupyter-book, see the chapter on Ridge and Lasso regression at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter2.html

Next week we will cover resampling methods and the bias-variance tradeoff, meaning you should have all ingredients for solving project 1. 

Best wishes to you all and don't hesitate to contact us in case you need help.