Plans for week 36

Dear all,  here are the plans for the coming week:

• Material for the active learning sessions on Tuesday and Wednesday
•      Summary from last week on discussion of SVD, Ridge and Lasso linear regression, first 30-40 mins of each session
•      Recommended Reading: Hastie et al chapter 3, see https://link.springer.com/book/10.1007/978-0-387-84858-7
•      Presentation and discussion of first project and exercises for week 36
• Material for the lecture on Thursday September 7
•      Linear Regression and links with Statistics, Resampling methods
•      Recommended Reading: Goodfellow et al chapter 3  (till 3.11) on probability theory, see https://www.deeplearningbook.org/
•      See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis), see https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/MachineLearningMurphy.pdfLinks to an external site.

The exercises and the weekly material can be found at the usual jupyter-book link https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.htmlLinks to an external site.. 

The first project will be available Monday morning. We will also send a suggestion to those of you who expressed an interest in collaborating with other course participants. We have a deadline till today (7pm) for compiling your answers in the google docs at https://docs.google.com/forms/d/12VNXJOqMfLGism580eBps_M7zk-gzXe7Qd-B2Ll_s8o/edit#responses

Best wishes to you all

Adam, Daniel, Fahimed, Ida, Karl-Henrik, Mia and Morten