diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 0a912a0a2..e383888aa 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree
index b8059a7e0..372a21886 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md
index 41568bb02..fc0e1c105 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -5,7 +5,7 @@ This course will be delivered in a hybrid mode, with online lectures and on site
 
 1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
 2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will  also be fully digital laboratory sessions for those who cannot attend;
-3. Three projects which are graded and count 1/3 each of the final grade;
+3. Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 11 for project 1, November 15 for project 2 and December 13 for project 3.
 4. A selected number of weekly assignments;
 5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
 6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
@@ -144,7 +144,7 @@ For the reading assignments we use the following abbreviations:
   - Hastie et al sections 9.1 and 9.2
 
 ###  Week 45 November 8-12 
-- Lab Wednesday: Work on project 3
+- Lab Wednesday: Work on project 2 and start project 3
 - Lecture Thursday: Ensemble methods, bagging and random forests
 - Lecture Friday: Boosting and gradient boosting
 - Reading recommendations:
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 65d532816..83737932a 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -388,7 +388,7 @@
 <ol class="simple">
 <li><p>Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;</p></li>
 <li><p>Two hours of laboratory sessions for work on computational projects and exercises for each group. There will  also be fully digital laboratory sessions for those who cannot attend;</p></li>
-<li><p>Three projects which are graded and count 1/3 each of the final grade;</p></li>
+<li><p>Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 11 for project 1, November 15 for project 2 and December 13 for project 3.</p></li>
 <li><p>A selected number of weekly assignments;</p></li>
 <li><p>The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;</p></li>
 <li><p>The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;</p></li>
@@ -613,7 +613,7 @@
 <div class="section" id="week-45-november-8-12">
 <h3>Week 45 November 8-12<a class="headerlink" href="#week-45-november-8-12" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
-<li><p>Lab Wednesday: Work on project 3</p></li>
+<li><p>Lab Wednesday: Work on project 2 and start project 3</p></li>
 <li><p>Lecture Thursday: Ensemble methods, bagging and random forests</p></li>
 <li><p>Lecture Friday: Boosting and gradient boosting</p></li>
 <li><p>Reading recommendations:</p>
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index d75361969..24e067173 100644
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class=\"section-number\">12. </span>Clustering Analysis","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","Content in Jupyter 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class=\"section-number\">12. </span>Clustering Analysis","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","Content in Jupyter Book","Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and 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\ No newline at end of file
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index 41568bb02..fc0e1c105 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -5,7 +5,7 @@ This course will be delivered in a hybrid mode, with online lectures and on site
 
 1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
 2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will  also be fully digital laboratory sessions for those who cannot attend;
-3. Three projects which are graded and count 1/3 each of the final grade;
+3. Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 11 for project 1, November 15 for project 2 and December 13 for project 3.
 4. A selected number of weekly assignments;
 5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
 6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
@@ -144,7 +144,7 @@ For the reading assignments we use the following abbreviations:
   - Hastie et al sections 9.1 and 9.2
 
 ###  Week 45 November 8-12 
-- Lab Wednesday: Work on project 3
+- Lab Wednesday: Work on project 2 and start project 3
 - Lecture Thursday: Ensemble methods, bagging and random forests
 - Lecture Friday: Boosting and gradient boosting
 - Reading recommendations: