Weekly plans and update for week 37

Hi everybody and welcome back.

What follows is our weekly digest with plans as well.

First I wanted to let you know that I am in the process of reorganizing the lecture material. As of now it looks more like chapters of a book. I will reorganize it as both a book and weekly lecture slides. Hopefully this will make it easier for you read the material beforehand.  As of now the material is subdivided into topics, and each file an be pretty long and difficult to navigate. I plan to have a first version of the lecture book by the end of this week. It will be updated continuously, and if you spot typos, inconsistencies etc it will be extremely helpful if you could convey them to us. Stay tuned thus. 

Else, this week we will end our discussion of Regression methods and discuss the mathematics and coding of Ridge and Lasso. This is covered by chapter 3.4 of Hastie et al at https://www.springer.com/gp/book/9780387848570. We will also discuss centering of data and normalization of data, see Hastie et al chapter 3.2.

Murphy's text at https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/MachineLearningMurphy.pdf covers much of the same in chapter 7.5

Last week we discussed resampling techniques such as cross-validation and bootstrap and the bias-variance trade-off. This is covered by the text of Hastie et in chapters 7.1-7.5 and 7.10 and 7.11

and we started discussing Ridge regression.

The material can also be found at the slides at https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression.html. Note that this material will be updated into weekly slides and a textbook by the end of this week.

Else, at the lab tomorrow, our focus is simply project 1!  Try to get started with parts a and b. 

Don't hesitate to ask in case you have questions and we obviously welcome all feedback. It can only improve the learning material.

Best wishes to you all,

Morten 

p.s. Stian Bilek will join us a TA, he just started with his PhD project now and is a wizard on ML. Remember also that lab session 8 is open, feel free to come.