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@@ -67,7 +67,7 @@ For the reading assignments we use the following abbreviations:
     - Video of Lecture, first part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16Firstpart.mp4?vrtx=view-as-webpage"
     - Video of Lecture, second part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16SecondPart.mp4?vrtx=view-as-webpage
 - Lecture Friday: More on Resampling methods and summary of linear regression
-
+    - Video of Lecture  at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage
 - Reading recommendations: 
   - Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index bca363031..cd20a5fcb 100644
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+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -470,7 +470,11 @@
 </li>
 </ul>
 </li>
-<li><p>Lecture Friday: More on Resampling methods and summary of linear regression</p></li>
+<li><p>Lecture Friday: More on Resampling methods and summary of linear regression</p>
+<ul>
+<li><p>Video of Lecture  at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage</a></p></li>
+</ul>
+</li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>Lectures on Resampling methods for week 37 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
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index 8e0b42c94..8d6889083 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 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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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 Grading","Textbooks"],titleterms:{"2021":17,"4155":13,"case":[6,8,16],"final":10,"function":[1,2,4,5,6,8,9,10,11,16],"import":[3,14,16],And:[],Eye:8,FYS:13,OLS:[3,4],RMS:11,The:[0,1,2,3,4,5,6,7,9,10,13,16],Useful:13,Using:11,activ:[2,10],actual:16,adaboost:8,adam:11,adapt:8,adding:4,adjust:2,again:7,aim:[6,7],algebra:14,algorithm:[0,7,8,9,10],algortithm:11,all:6,analys:3,analysi:[0,1,3,4,9,13,16],ani:11,anoth:7,appli:13,approach:[1,6],approxim:10,architectur:2,arrai:14,assist:17,august:15,autocorrel:16,automat:11,back:[2,9,10],background:13,bag:8,base:11,basic:[0,1,3,5,7,8,9,14],batch:2,bay:3,befor:9,better:[6,16],bia:4,binari:2,binomi:16,bird:8,block:16,book:12,boost:8,bootstrap:[4,8,16],boston:1,breast:2,brief:[],bring:10,build:[2,7],calcul:16,cancer:[2,5,7,9],cart:7,central:[11,13,16],chain:10,chang:8,chi:1,choos:2,classic:9,classif:[2,7,8],classifi:6,clip:2,cluster:0,code:[0,1,2,3,7,9,10,16],collect:2,compar:8,complex:[1,4],compon:9,comput:[7,16],con:7,concept:16,condit:[],conjug:11,content:12,continu:16,convex:[6,11],convolut:10,correl:[9,16],correspond:[],cost:[2,8],cours:[13,18],covari:[3,9,16],cross:4,cumul:16,cython:[],data:[1,2,4,5,7,9,13,16],dataset:2,decemb:15,decis:[7,8],decomposit:[3,9,14],deep:2,defin:2,definit:16,degre:1,demonstr:16,dens:1,deriv:[3,10],descent:[8,11],develop:2,deviat:16,diagon:9,dice:16,differ:6,differenti:11,dimension:6,disadvantag:7,discret:16,disguis:16,distribut:[3,16],doing:2,domain:16,down:2,dropout:2,economi:[],element:[1,16],elimin:14,ensembl:8,entropi:7,environ:1,equat:[1,10],error:[1,8],etc:[],evalu:2,event:16,exampl:[1,2,5,6,7,8,16],exercis:[1,4],expect:16,experi:16,explor:1,exponenti:16,express:[],extend:[],extrem:8,fall:17,famili:2,famou:16,fantast:[],featur:[7,14],feed:[2,10],fine:2,first:10,fit:[1,8],forest:8,forward:[2,10],frank:4,freedom:1,frequentist:1,fridai:[],from:[3,8,10],further:3,gaussian:[14,16],gener:[7,16],geometr:9,gini:7,good:1,grade:17,gradient:[2,8,11],handl:14,has:13,hessian:[],homework:[],hous:1,how:16,hyperparamet:2,hyperplan:6,id3:7,idea:[0,9],ideal:[],implement:[2,16],implic:3,improv:2,increment:9,index:7,inform:17,instal:13,instructor:17,interpret:[3,9],introduc:9,introduct:[1,4,13,14],invers:[3,14],iter:8,its:16,jackknif:16,julia:[],jungl:8,jupyt:12,kera:2,kernel:[6,9],lagrangian:6,lasso:[3,4],later:3,layer:[2,10],learn:[1,2,9,11,13],least:[3,4],level:8,librari:13,likelihood:5,limit:[2,11,16],linear:[1,6,11,14],link:[3,9,15,18],logist:5,loss:[],machin:[1,6,11,13],main:16,make:[1,7,8],mani:[8,10],materi:15,math:3,mathemat:[3,6],matric:3,matrix:[2,3,9,10,14,16],matter:1,mean:[0,1,16],meet:[3,8,16],mercer:6,mersenn:16,method:[4,7,8,11,16],mlp:10,model:[1,2,10],moment:16,momentum:11,moon:[6,7],more:[0,4,14],multilay:10,multipl:2,multipli:6,name:16,need:[],network:[2,5,10],neural:[2,10],newton:[],non:6,normal:[1,2,16],norwai:13,notat:10,novemb:15,now:[2,7],nuclear:1,nueral:5,numba:[],number:[1,16],numer:16,numpi:14,numpython:0,observ:16,obtain:9,octob:15,off:4,one:10,optim:[2,6,11,13],ordinari:[3,4],organ:1,oslo:[13,18],other:[7,9,10,16],our:[0,1,3,9,11,16],outcom:13,output:16,overarch:[1,6,7],overview:8,own:[0,1,8,9],packag:14,panda:[],part:[11,13],pass:2,pca:9,pdf:16,perceptron:10,perform:[2,7],period:16,perspect:2,poisson:16,practic:11,pre:2,preprocess:[],prerequisit:13,princip:9,pro:7,probabl:[3,16],problem:[2,11],procedur:7,process:2,program:11,project:4,prop:11,propag:[2,10],properti:[3,16],pseudo:16,python:[0,1,7,13,14,16],quick:6,ran0:16,random:[8,9,16],raphson:[],read:7,real:4,recip:16,recurr:10,reduc:1,regress:[1,3,4,5,7,8,11],regular:2,relev:18,relu:2,remind:[4,6],requir:13,resampl:4,revisit:11,ridg:[3,4],rng:16,rule:10,sampl:[9,16],schedul:15,schemat:7,scikit:[1,2,9],select:16,semest:17,sensit:[],septemb:15,set:[1,7,10],sgd:[],should:[2,16],simpl:[1,7,11,16],singl:8,singular:[3,9],situat:16,size:[],slightli:[],soft:6,softmax:2,softwar:[],solv:[],solver:11,some:11,split:1,squar:[1,3,4,8],standard:[11,16],state:1,statist:[3,4,13,16],steepest:[8,11],step:[],stk3155:13,stochast:[11,16],stop:[],supervis:2,support:6,svd:3,teach:[15,17],teacher:17,techniqu:[4,9],technolog:13,tensorflow:2,test:[1,2],textbook:18,than:[],theorem:[3,6,9,10,16],theori:16,three:16,tip:11,togeth:10,top:2,toss:16,toward:[0,9],trade:4,tradeoff:4,train:[1,2],tree:[7,8],tune:2,two:[6,13],type:10,uncorrel:16,understand:[],uniform:16,univers:[10,13,18],use:[2,16],used:5,using:[1,16],valid:4,valu:[3,9,16],variabl:16,varianc:[4,16],variou:1,vector:[6,10,14],view:[1,8],visual:[2,7],wai:7,week:15,weekli:15,what:[1,16],when:[],which:[2,16],why:16,wisconsin:5,write:[0,9],xgboost:8,your:[1,8]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index caaef2d59..a33d7bf3e 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -67,7 +67,7 @@ For the reading assignments we use the following abbreviations:
     - Video of Lecture, first part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16Firstpart.mp4?vrtx=view-as-webpage"
     - Video of Lecture, second part at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember16SecondPart.mp4?vrtx=view-as-webpage
 - Lecture Friday: More on Resampling methods and summary of linear regression
-
+    - Video of Lecture  at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember17.mp4?vrtx=view-as-webpage
 - Reading recommendations: 
   - Lectures on Resampling methods for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Bishop 1.3 (cross-validation) and 3.2 (bias-variance tradeoff)