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Morten Hjorth-Jensen
2021-05-11 13:17:18 -04:00
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@@ -30,7 +30,7 @@ This course aims at giving you insights and knowledge about many of the central
## Prerequisites
Basic knowledge in programming and mathematics, with an emphasis on linear algebra. Knowledge of Python or/and C++ as programming languages is strongly recommended and experience with Jupiter notebook is recommended. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities offer nowadays a basic programming course (often compulsory) where Python is the recurring programming language.
We recommend also refreshing your knowledge on Statistics and Probability theory. The lecture notes at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html offer a review of Statistics and Probability theory.
## The course has two central parts
@@ -178,7 +178,8 @@ Here we list several useful Python libraries we strongly recommend (if you use a
## Textbooks
_Recommended textbooks_:
The books of Bishop and Goodfellow et are the recommended text. We will follow these texts closely and the weekly reading assignments refer to these two texts.
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to th 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.
- 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.