From e8b07c94aa36d197dcee64ac20945dbfce3b55c7 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Tue, 23 Aug 2022 12:16:21 +0200
Subject: [PATCH] updated schedule

---
 .../_build/.doctrees/environment.pickle       | Bin 200831 -> 201591 bytes
 .../_build/.doctrees/teachers.doctree         | Bin 12532 -> 12532 bytes
 .../_build/html/_sources/teachers.md          |   2 +-
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/teachers.html    |  12 ++++++------
 doc/LectureNotes/teachers.md                  |   2 +-
 6 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index c244c906021eeac49e7e427b9cea67d8acd7eb61..42abc5ce0bbe12b7591fd9ba1bee10ec624e84d1 100644
GIT binary patch
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diff --git a/doc/LectureNotes/_build/.doctrees/teachers.doctree b/doc/LectureNotes/_build/.doctrees/teachers.doctree
index babe713e02330b4fbf376b4f3370dda2592cbb34..10781264ba19862f3633395a3662094374dfeab7 100644
GIT binary patch
delta 40
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delta 40
vcmey8_$6_J883(7lnnORDH&pu`FYhD4L7Ipvhpz+PR@}w1ajs{hlv6J8M_P+

diff --git a/doc/LectureNotes/_build/html/_sources/teachers.md b/doc/LectureNotes/_build/html/_sources/teachers.md
index 1a65c3772..9aece39a3 100644
--- a/doc/LectureNotes/_build/html/_sources/teachers.md
+++ b/doc/LectureNotes/_build/html/_sources/teachers.md
@@ -8,7 +8,7 @@
 * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470 
 * _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.
 
-##  Teaching Assistants Fall semester 2021
+##  Teaching Assistants Fall semester 2022
 * Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no
 * Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no
 * Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index bcfbe30c6..8ab2562e2 100644
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<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","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<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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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<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","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<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 Grading","Textbooks"],titleterms:{"1":[],"10":17,"11":17,"12":17,"13":[],"14":17,"15":[],"16":17,"17":17,"18":17,"19":17,"2":[0,17],"20":[],"2021":[],"2022":19,"21":17,"22":17,"23":17,"24":17,"25":17,"26":17,"27":[],"28":17,"29":17,"3":17,"30":17,"31":17,"34":17,"35":17,"36":17,"37":17,"38":17,"39":17,"4":17,"40":17,"41":17,"4155":[],"42":17,"43":17,"44":17,"45":17,"46":17,"47":17,"5":17,"6":[],"7":17,"8":[],"9":17,"case":[8,10,18],"do":1,"final":12,"function":[0,1,6,7,8,10,11,12,13,18],"import":[5,16,18],"new":4,A:[0,1,4,8,9],And:13,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,15,18],With:4,activ:[1,12],actual:18,ad:6,adaboost:10,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9],aim:[8,9],algebra:16,algorithm:[9,10,11,12],algortithm:13,all:8,an:[0,4,10],analys:5,analysi:[0,5,6,11,15,18],ani:13,anoth:9,appli:15,approach:[0,8,14],approxim:12,architectur:1,arrai:16,assist:19,august:17,autocorrel:18,autograd:[2,13],automat:13,back:[1,11,12],background:15,bag:10,base:13,basic:[0,5,7,9,10,11,16],batch:1,bay:5,befor:11,better:[8,18],bia:6,binari:1,binomi:18,bird:10,block:18,boost:10,bootstrap:[6,10,18],boston:0,breast:1,bring:12,build:[1,3,9],calcul:18,cancer:[1,7,9,11],cart:9,central:[13,15,18],chain:12,chang:10,chi:0,choos:1,cifar01:3,classic:11,classif:[1,9,10],classifi:8,clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,14,18],collect:[1,3],compar:[2,10],complex:[0,6],complic:6,compon:11,comput:[9,18],con:9,concept:18,conjug:13,continu:18,convex:[8,13],convolut:[3,12],correl:[11,18],cost:[1,10],cours:[15,20],covari:[5,11,18],cross:6,cumul:18,data:[0,1,3,6,7,9,11,15,18],dataset:[1,3],decai:2,decis:[9,10],decomposit:[5,11,16],deep:[1,2],defin:1,definit:18,degre:0,demonstr:18,dens:0,deriv:[5,12],descent:[2,10,13],detail:3,develop:1,deviat:18,diagon:11,dice:18,differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8],disadvantag:9,discret:18,disguis:18,distribut:[5,18],domain:18,down:1,dropout:1,element:[0,18],elimin:16,ensembl:10,entropi:9,environ:0,equat:[0,2,12],error:[0,10],euler:2,evalu:1,event:18,exampl:[0,1,2,3,4,6,7,8,9,10,18],exercis:[0,6],expect:18,experi:18,explor:0,exponenti:[2,18],extrapol:4,extrem:10,ey:10,fall:19,famili:1,famou:18,featur:[9,16],feed:[1,12],fine:1,first:[4,12],fit:[0,10],forc:3,forest:10,forward:[1,2,12],fourier:3,frank:6,freedom:0,frequentist:0,from:[5,10,12],full:2,further:[3,5],fy:[],gan:4,gaussian:[16,18],gd:13,gener:[4,9,18],geometr:11,gini:9,good:0,grade:19,gradient:[1,2,10,13],growth:2,ha:15,handl:16,hidden:2,hous:0,how:18,hyperparamet:1,hyperplan:8,i:[1,18],id3:9,idea:11,implement:[1,18],implic:5,improv:1,includ:13,increment:11,index:9,inform:19,input:2,instal:15,instructor:19,interpret:[5,11],introduc:11,introduct:[0,6,15,16],invers:[5,16],iter:10,its:18,jackknif:18,jungl:10,kera:[1,3],kernel:[8,11],lagrangian:8,lasso:[5,6],later:5,layer:[1,2,3,12],learn:[0,1,2,11,13,14,15],least:[5,6],level:10,librari:15,likelihood:7,limit:[1,13,18],linear:[0,8,13,16],link:[5,11,17,20],logist:[7,13],lu:16,machin:[0,8,13,15],main:18,make:[0,9,10],mani:[10,12],materi:17,math:5,mathemat:[3,5,8],matric:5,matrix:[1,5,11,12,16,18],matter:0,mean:[0,18],meet:[5,10,18],mercer:8,mersenn:18,method:[6,9,10,13,18],mlp:12,mnist:[3,4],model:[0,1,4,6,12],moment:18,momentum:13,moon:[8,9],more:[3,6,16],multilay:12,multipl:[1,3],multipli:8,name:18,network:[1,2,3,4,7,12],neural:[1,2,3,4,12],non:8,normal:[0,1,18],norwai:[],notat:12,novemb:17,now:[1,9],nuclear:0,nueral:7,number:[0,2,18],numer:[2,18],numpi:16,object:3,observ:18,obtain:11,octob:17,od:2,off:6,ol:[5,6,13],one:[2,12],oper:16,optim:[1,8,13,15],ordinari:[5,6],organ:0,oslo:20,other:[4,9,11,12,16,18],our:[0,4,5,11,13,18],outcom:15,output:[2,18],overarch:[0,4,8,9],overview:10,own:[0,10,11],packag:16,part:[13,15],partial:2,pass:1,pca:11,pdf:18,perceptron:12,perform:[1,9],period:[3,18],perspect:1,point:4,poisson:[2,18],polynomi:3,popul:2,practic:13,pre:[1,3],predict:4,prerequisit:[3,15],princip:11,principl:3,pro:9,probabl:[5,18],problem:[1,2,13],procedur:9,process:[1,3],program:[2,13],project:6,prop:13,propag:[1,12],properti:[5,18],pseudo:18,python:[0,9,15,16,18],quick:8,ran0:18,random:[10,11,18],read:9,real:6,recip:18,recurr:[4,12],reduc:0,reduct:3,reformul:2,regress:[0,5,6,7,9,10,13],regular:1,relev:20,relu:1,remark:3,remind:[6,8],requir:[2,15],resampl:6,rescal:6,resourc:2,revisit:13,ridg:[5,6],rm:13,rng:18,rule:12,s:[8,10,18],sampl:[11,18],schedul:17,schemat:9,scheme:2,scikit:[0,1,11],select:18,semest:19,septemb:17,set:[0,2,3,9,12],sgd:13,should:[1,18],simpl:[0,4,9,13,18],singl:10,singular:[5,11],situat:18,soft:8,softmax:1,solv:2,solver:13,some:13,specifi:2,split:0,squar:[0,5,6,10],standard:[13,18],state:0,statist:[5,6,15,18],steepest:[10,13],stk3155:[],stochast:[13,18],superposit:3,supervis:1,support:8,svd:5,systemat:3,teach:[17,19],teacher:19,techniqu:[6,11],technolog:15,tensorflow:[1,3],test:[0,1],textbook:20,theorem:[5,8,11,12,18],theori:18,three:18,tip:13,togeth:12,top:1,toss:18,toward:11,trade:6,tradeoff:6,train:[0,1,4],transform:3,tree:[9,10],tune:1,two:[3,8,15],type:[2,4,12],uncorrel:18,uniform:18,univers:[12,20],unsupervis:14,up:[0,2,9,12],us:[0,1,2,3,7,13,15,18],valid:6,valu:[5,11,18],variabl:18,varianc:[6,18],variou:0,vector:[8,12,16],view:[0,4,10],visual:[1,9],vs:3,wai:9,wave:2,week:17,weekli:17,what:[0,18],which:[1,18],why:18,wisconsin:7,write:[4,11],xgboost:10,your:[0,10]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index 90a44ede5..dfa595018 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -313,8 +313,8 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2021">
-   Teaching Assistants Fall semester 2021
+  <a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2022">
+   Teaching Assistants Fall semester 2022
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -348,8 +348,8 @@ const thebe_selector_output = ".output, .cell_output"
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2021">
-   Teaching Assistants Fall semester 2021
+  <a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2022">
+   Teaching Assistants Fall semester 2022
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -378,8 +378,8 @@ const thebe_selector_output = ".output, .cell_output"
 <li><p><em>Office hours</em>: <em>Anytime</em>! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.</p></li>
 </ul>
 </div>
-<div class="section" id="teaching-assistants-fall-semester-2021">
-<h2>Teaching Assistants Fall semester 2021<a class="headerlink" href="#teaching-assistants-fall-semester-2021" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="teaching-assistants-fall-semester-2022">
+<h2>Teaching Assistants Fall semester 2022<a class="headerlink" href="#teaching-assistants-fall-semester-2022" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li><p>Bennosh Ashrafi, <a class="reference external" href="mailto:behnoosh&#46;ashrafi&#37;&#52;&#48;fys&#46;uio&#46;no">behnoosh<span>&#46;</span>ashrafi<span>&#64;</span>fys<span>&#46;</span>uio<span>&#46;</span>no</a></p></li>
 <li><p>Frida Marie Engøy Westbye, <a class="reference external" href="mailto:f&#46;m&#46;e&#46;westby&#37;&#52;&#48;fys&#46;uio&#46;no">f<span>&#46;</span>m<span>&#46;</span>e<span>&#46;</span>westby<span>&#64;</span>fys<span>&#46;</span>uio<span>&#46;</span>no</a></p></li>
diff --git a/doc/LectureNotes/teachers.md b/doc/LectureNotes/teachers.md
index 1a65c3772..9aece39a3 100644
--- a/doc/LectureNotes/teachers.md
+++ b/doc/LectureNotes/teachers.md
@@ -8,7 +8,7 @@
 * _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470 
 * _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.
 
-##  Teaching Assistants Fall semester 2021
+##  Teaching Assistants Fall semester 2022
 * Bennosh Ashrafi, behnoosh.ashrafi@fys.uio.no
 * Frida Marie Engøy Westbye, f.m.e.westby@fys.uio.no
 * Ida Torkjellsdatter Storehaug, i.t.storehaug@fys.uio.no