Plans for week 35
Dear all, first of all we hope you had an excellent weekend. we look much forward to welcome you back to this week's exercises and lectures.
This week we will discuss and work on the exercises for week 35 (all relevant for the start of project 1 next week). The material needed for these exercises is covered by the first part of the weekly slides for week 35, see the material before the heading Material for lecture Thursday, August 31. This refers to the material till slide 41 in for example the https://compphysics.github.io/MachineLearning/doc/pub/week35/html/._week35-bs041.html
The slides 1-41 contain also several examples and derivations relevant for solving the three exercises we will work on this week during the lab sessions.
Please do read this material before coming to the various lab sessions. Also, please do send us your questions before the sessions start. We will spend 15-30 mins at the start of each session to discuss this material, before we dedicate the rest of the time to the solution of the exercises.
The exercises are at https://github.com/CompPhysics/MachineLearning/blob/master/doc/LectureNotes/exercisesweek35.ipynb
Thursdays we will always have a regular lecture and we will cover new material relevant for next week. The emphasis this week is on the mathematics of linear regression and we will also discuss statistical properties like the covariance and start our discussions of Ridge and Lasso regression. The lecture on Thursday is available via zoom and will also be recorded.
Besides the lecture slides and notes, we recommend also reading
- Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (overview)
- Hastie, Tibshirani and Friedman, The elements of statistical learning, sections 3.1-3.4 of relevance for the mathematical analysis of linear regression.
Best wishes to you all,
Morten et al