Plans for week 35, August 26-30
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.
Important note: since the lecture hall we have been assigned for the Monday sessions is not yet ready with all AV equipment, we will use our back-up auditorium in the chemistry building, Auditorium 2 till approximately mid September. We are very sorry for this and hope it won't cause too many problems.
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 at 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
Monday 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 Monday is available via zoom and will also be recorded.
Besides the lecture slides and notes, we recommend also reading or taking a look at
- Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (overview)
- Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
- For exercise 1 of week 35, the book by
A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at https://mml-book.github.io/ (section 5.5 on derivatives) is very useful for exercise 1 this coming week.
Best wishes to you all,
Morten et al