Plans for week 35, August 29 -September 2

Welcome to a new week to you all. 

This week the plans are as follows

o Lab Wednesday: Work on exercises 1-5 for week 35, see end of weekly slides for the exercises, https://compphysics.github.io/MachineLearning/doc/pub/week35/html/week35.html. These exercises are not mandatory and don't stress if you don't finish them all. We can easily continue with these exercises next week as well. They serve as a basis for the first project.

o Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition

o  Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition

 Reading recommendations:

  o See lecture notes for week 35 at URL:"https://compphysics.github.io/MachineLearning/doc/web/course.html"

  o For a review on statistics see the jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/statistics.html", the   most relevant parts are covered by sections 1.1.1-1.1.5

  o Hastie et al chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1

  o Bishop sections 1.1 and 3.1

  o A good review on statistics is given by Murphy's text, chapter 2, see URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/MachineLearningMurphy.pdf"

The main topics on Thursday and Friday are:

o Repetition from last week on linear regression

o Reminder on statistics with quantities like mean values, variance and covariance

o Discussion of how to prepare data and examples of applications of linear regression

o Mathematical interpretations of Linear Regression

o Start discussing Ridge and Lasso regression and Singular Value Decomposition.

The videos of the lectures from last week are also available with subtitles. You can find the links to the videos either at link for the weekly lectures or the weekly schedule at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/schedule.html

The videos are just raw dumps of what was done during that specific lectures, but hopefully they can be of use to those of you who cannot attend the lectures (either in person or via zoom). Or just in case you wish to revisit what was discussed during that specific lectures.

If you come across typos/errors etc in the lecture material, please let us know. Also, if you come across videos of interest, software etc, feel free to share. 

Best wishes to you all.