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Update week34.do.txt

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Morten Hjorth-Jensen
2024-06-22 11:21:20 +02:00
parent 99aff529dc
commit 9fd33a9104
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doc/src/week34/week34.do.txt
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@@ -13,7 +13,7 @@ o There are four groups:
* Tuesdays 815am-12pm and 1215pm-4pm
* Wednesdays 815am-12pm and 1215pm-4pm.
o On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.
o On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics as well as solving specific problems. These lectures will also be recorded.
* "Link to recording of lecture TBA":"https://youtu.be/"
The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learning material with reading suggestions will be made available before each week starts.
@@ -90,9 +90,9 @@ The labs are also available till 6pm Tuesdays and Wednesdays. Videos and learnin
===== Deadlines for projects (tentative) =====
o Project 1: October 15 (available September 4) graded with feedback)
o Project 2: November 13 (available October 8, graded with feedback)
o Project 3: December 11 (available November 12, graded with feedback)
o Project 1: October 7 (available September 2) graded with feedback)
o Project 2: November 4 (available October 8, graded with feedback)
o Project 3: December 9 (available November 5, graded with feedback)
@@ -124,7 +124,7 @@ In addition you can get an extra 10% score for weekly assignments (10 in total a
!bblock
The lecture notes are collected as a jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html".
In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.
In addition to the lecture notes, we recommend the books of Rasckha et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community.
!eblock
!split