From 97f8751fc396909068ca7309e33e2ad41bbea116 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Wed, 7 Jul 2021 22:55:23 +0200 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8bfd544c3..3f77a685f 100644 --- a/README.md +++ b/README.md @@ -181,7 +181,7 @@ Here we list several useful Python libraries we strongly recommend (if you use a _Recommended textbooks_: The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts. - Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. If you login to the University Library or access this site via a University IP number, you can download for free the textbook in PDF format or epub format. -- We recommend also the text of Ian Goodfellow and Yoshua Bengio and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. +- We recommend the text of Ian Goodfellow and Yoshua Bengio and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks. _Additional textbooks_: - Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.