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Morten Hjorth-Jensen
2025-07-16 09:06:52 +02:00
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@@ -103,8 +103,6 @@ possible.
* _Phone_: +47-48257387
* _Office_: Department of Physics, room FØ470
* _Office hours_: *Anytime*! Individual or group office hours can be performed either in person or via zoom. Feel free to send an email for planning.
* Sigurd k. Huse, s.k.huse@fys.uio.no
* Odin Johansen, odin.johansen@fys.uio.no
* Fahimeh Najafi, fahimeh.najafi@fys.uio.no
* Mia-Katrin Ose Kvalsund, m.k.o.kvalsund@fys.uio.no
* Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
@@ -145,9 +143,9 @@ The final number of points is based on the average of all projects (including ev
| Activity | Fraction of total grade |
|------|-----|
| First project, _due October 7_ | 1/3 |
| Second project, _due November 4_ | 1/3 |
| Third project, _due December 9_ | 1/3 |
| First project, _due October 6_ | 1/3 |
| Second project, _due November 3_ | 1/3 |
| Third project, _due December 8_ | 1/3 |
| Extra Credit (not mandatory), weekly exercise assignments, 10 in total (due Fridays)| 10% |
@@ -244,7 +242,7 @@ the weekly reading assignments refer to these two texts.
- Ian Goodfellow, 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 good extent this text.
- Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312, see also https://sebastianraschka.com/blog/2022/ml-pytorch-book.html
The text by Raschka et al. is well-adapted to this text and contains many coding examples.
The text by Raschka et al. is well-adapted to this course and contains many coding examples.
The weekly plans will include reading suggestions from these two textbooks. In addition, you may find the following textbooks interesting.
_Additional textbooks_: