diff --git a/README.md b/README.md
index 1a66beaac..44a433a76 100644
--- a/README.md
+++ b/README.md
@@ -310,7 +310,7 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)
 - Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage
 - Lecture Friday: Logistic Regression and start discussions of gradient optimization
-
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember24.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings  for logistic regression
diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index ca2dc039c..4b0d13b92 100644
Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ
diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree
index 0d58348e4..4ba59f04a 100644
Binary files a/doc/LectureNotes/_build/.doctrees/schedule.doctree and b/doc/LectureNotes/_build/.doctrees/schedule.doctree differ
diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md
index 8fb9c6ab3..6ae2ca007 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -80,7 +80,7 @@ For the reading assignments we use the following abbreviations:
 - Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage
 - Lecture Friday: Logistic Regression and gradient optimization
-
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember24.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings  for logistic regression
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 5202fa593..215ef81cf 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -494,7 +494,11 @@
 <li><p>Video of Lecture at <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage</a></p></li>
 </ul>
 </li>
-<li><p>Lecture Friday: Logistic Regression and gradient optimization</p></li>
+<li><p>Lecture Friday: Logistic Regression and gradient optimization</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/LectureSeptember24.mp4?vrtx=view-as-webpage">https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember24.mp4?vrtx=view-as-webpage</a></p></li>
+</ul>
+</li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 38 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 3510073c9..e2a40e15a 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,Ising:4,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],complic: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:[1,2,4,5,6,7,8,16],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:[1,2,4,10],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,rescal: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 8fb9c6ab3..6ae2ca007 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -80,7 +80,7 @@ For the reading assignments we use the following abbreviations:
 - Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureSeptember23.mp4?vrtx=view-as-webpage
 - Lecture Friday: Logistic Regression and gradient optimization
-
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember24.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Bishop 4.1, 4.2 and 4.3. Not all the material is relevant or will be covered. Section 4.3 is the most relevant, but 4.1 and 4.2 give interesting background readings  for logistic regression