From 6fef28a7b852bfda7ac8a51d9376ccdd60d627a1 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Wed, 27 Oct 2021 07:21:07 +0200
Subject: [PATCH] small update on lecture notes and schedule

---
 .../_build/.doctrees/environment.pickle       | Bin 187159 -> 187341 bytes
 .../_build/.doctrees/schedule.doctree         | Bin 74945 -> 75441 bytes
 .../_build/html/_sources/schedule.md          |   3 ++-
 doc/LectureNotes/_build/html/schedule.html    |   3 ++-
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/schedule.md                  |   3 ++-
 6 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index f152a071ee81d1677d554b129e19dbf27ea3f8a5..48aea9d64b10a633f344e6082d562c4a3e2fbf98 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/schedule.doctree b/doc/LectureNotes/_build/.doctrees/schedule.doctree
index c584aa4392e87116097ea89527314b43e3338716..acda3c778707418693676355ec02a0ab31fb60ea 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md
index e925d1209..e65587b6a 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -134,10 +134,11 @@ For the reading assignments we use the following abbreviations:
 ###  Week 43 October 25-29
 - Lab Wednesday: Work on project 2
 - Lecture Thursday:  Recurrent Neural Networks
-- Lecture Friday: Recurrent Neural Networks and time series
+- Lecture Friday: Recurrent Neural Networks and principal component analysis (PCA)
 - Reading recommendations:
   - See lecture notes for week 43 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications
+  - For PCA see lecture notes (jupyter book) chapter 11 and also chapter 12 on clustering. Geron's chapter 8 is also a good read.
 
 ###  Week 44 November 1-5
 - Lab Wednesday: Work on project 2
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 6ef3e56d3..5f2f1a901 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -608,11 +608,12 @@
 <ul class="simple">
 <li><p>Lab Wednesday: Work on project 2</p></li>
 <li><p>Lecture Thursday:  Recurrent Neural Networks</p></li>
-<li><p>Lecture Friday: Recurrent Neural Networks and time series</p></li>
+<li><p>Lecture Friday: Recurrent Neural Networks and principal component analysis (PCA)</p></li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 43 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>
 <li><p>For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications</p></li>
+<li><p>For PCA see lecture notes (jupyter book) chapter 11 and also chapter 12 on clustering. Geron’s chapter 8 is also a good read.</p></li>
 </ul>
 </li>
 </ul>
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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\">15. </span>Solving Differential Equations  with Deep Learning","<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","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\">15. </span>Solving Differential Equations  with Deep Learning","<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","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":[7,9,16],"final":11,"function":[1,2,5,6,7,9,10,11,12,16],"import":[4,14,16],And:[],Eye:9,FYS:13,Ising:5,ODE:3,OLS:[4,5],RMS:12,The:[0,1,2,3,4,5,6,7,8,10,11,13,16],Useful:13,Using:[3,12],activ:[2,11],actual:16,adaboost:9,adam:12,adapt:9,adding:5,adjust:2,again:8,aim:[7,8],algebra:14,algorithm:[0,8,9,10,11],algortithm:12,all:7,analys:4,analysi:[0,1,4,5,10,13,16],ani:12,anoth:8,appli:13,approach:[1,7],approxim:11,architectur:2,arrai:14,assist:17,august:15,autocorrel:16,autograd:3,automat:12,back:[2,10,11],background:13,bag:9,base:12,basic:[0,1,4,6,8,9,10,14],batch:2,bay:4,befor:10,better:[7,16],bia:5,binari:2,binomi:16,bird:9,block:16,book:[],boost:9,bootstrap:[5,9,16],boston:1,breast:2,brief:[],bring:11,build:[2,8],calcul:16,cancer:[2,6,8,10],cart:8,central:[12,13,16],chain:11,chang:9,chi:1,choos:2,classic:10,classif:[2,8,9],classifi:7,clip:2,cluster:0,code:[0,1,2,3,4,8,10,11,16],collect:2,compar:[3,9],complex:[1,5],complic:5,compon:10,comput:[8,16],con:8,concept:16,condit:[],conjug:12,content:[],continu:16,convex:[7,12],convolut:11,correl:[10,16],correspond:[],cost:[2,9],cours:[13,18],covari:[4,10,16],cross:5,cumul:16,cython:[],data:[1,2,5,6,8,10,13,16],dataset:2,decai:3,decemb:15,decis:[8,9],decomposit:[4,10,14],deep:[2,3],defin:2,definit:16,degre:1,demonstr:16,dens:1,deriv:[4,11],descent:[3,9,12],develop:2,deviat:16,diagon:10,dice:16,differ:7,differenti:[3,12],diffus:3,dimension:[3,7],disadvantag:8,discret:16,disguis:16,distribut:[4,16],doing:2,domain:16,down:2,dropout:2,economi:[],element:[1,16],elimin:14,ensembl:9,entropi:8,environ:1,equat:[1,3,11],error:[1,9],etc:[],euler:3,evalu:2,event:16,exampl:[1,2,3,5,6,7,8,9,16],exercis:[1,5],expect:16,experi:16,explor:1,exponenti:[3,16],express:[],extend:[],extrem:9,fall:17,famili:2,famou:16,fantast:[],featur:[8,14],feed:[2,11],fine:2,first:11,fit:[1,9],forest:9,forward:[2,3,11],frank:5,freedom:1,frequentist:1,fridai:[],from:[4,9,11],full:3,further:4,gaussian:[14,16],gener:[8,16],geometr:10,gini:8,good:1,grade:17,gradient:[2,3,9,12],growth:3,handl:14,has:13,hessian:[],hidden:3,homework:[],hous:1,how:16,hyperparamet:2,hyperplan:7,id3:8,idea:[0,10],ideal:[],implement:[2,16],implic:4,improv:2,increment:10,index:8,inform:17,input:3,instal:13,instructor:17,interpret:[4,10],introduc:10,introduct:[1,5,13,14],invers:[4,14],iter:9,its:16,jackknif:16,julia:[],jungl:9,jupyt:[],kera:2,kernel:[7,10],lagrangian:7,lasso:[4,5],later:4,layer:[2,3,11],learn:[1,2,3,10,12,13],least:[4,5],level:9,librari:13,likelihood:6,limit:[2,12,16],linear:[1,7,12,14],link:[4,10,15,18],logist:6,loss:[],machin:[1,7,12,13],main:16,make:[1,8,9],mani:[9,11],materi:15,math:4,mathemat:[4,7],matric:4,matrix:[2,4,10,11,14,16],matter:1,mean:[0,1,16],meet:[4,9,16],mercer:7,mersenn:16,method:[5,8,9,12,16],mlp:11,model:[1,2,5,11],moment:16,momentum:12,moon:[7,8],more:[0,5,14],multilay:11,multipl:2,multipli:7,name:16,need:[],network:[2,3,6,11],neural:[2,3,11],newton:[],non:7,normal:[1,2,16],norwai:13,notat:11,novemb:15,now:[2,8],nuclear:1,nueral:6,numba:[],number:[1,3,16],numer:[3,16],numpi:14,numpython:0,observ:16,obtain:10,octob:15,off:5,one:[3,11],oper:14,optim:[2,7,12,13],ordinari:[4,5],organ:1,oslo:[13,18],other:[8,10,11,14,16],our:[0,1,4,10,12,16],outcom:13,output:[3,16],overarch:[1,7,8],overview:9,own:[0,1,9,10],packag:14,panda:[],part:[12,13],partial:3,pass:2,pca:10,pdf:16,perceptron:11,perform:[2,8],period:16,perspect:2,poisson:[3,16],popul:3,practic:12,pre:2,preprocess:[],prerequisit:13,princip:10,pro:8,probabl:[4,16],problem:[2,3,12],procedur:8,process:2,program:[3,12],project:5,prop:12,propag:[2,11],properti:[4,16],pseudo:16,python:[0,1,8,13,14,16],quick:7,ran0:16,random:[9,10,16],raphson:[],read:8,real:5,recip:16,recurr:11,reduc:1,reformul:3,regress:[1,4,5,6,8,9,12],regular:2,relev:18,relu:2,remind:[5,7],requir:[3,13],resampl:5,rescal:5,resourc:3,revisit:12,ridg:[4,5],rng:16,rule:11,sampl:[10,16],schedul:15,schemat:8,scheme:3,scikit:[1,2,10],select:16,semest:17,sensit:[],septemb:15,set:[1,3,8,11],sgd:[],should:[2,16],simpl:[1,8,12,16],singl:9,singular:[4,10],situat:16,size:[],slightli:[],soft:7,softmax:2,softwar:[],solv:3,solver:12,some:12,specifi:3,split:1,squar:[1,4,5,9],standard:[12,16],state:1,statist:[4,5,13,16],steepest:[9,12],step:[],stk3155:13,stochast:[12,16],stop:[],supervis:2,support:7,svd:4,teach:[15,17],teacher:17,techniqu:[5,10],technolog:13,tensorflow:2,test:[1,2],textbook:18,than:[],theorem:[4,7,10,11,16],theori:16,three:16,tip:12,togeth:11,top:2,toss:16,toward:[0,10],trade:5,tradeoff:5,train:[1,2],tree:[8,9],tune:2,two:[7,13],type:[3,11],uncorrel:16,understand:[],uniform:16,univers:[11,13,18],use:[2,16],used:6,using:[1,3,16],valid:5,valu:[4,10,16],variabl:16,varianc:[5,16],variou:1,vector:[7,11,14],view:[1,9],visual:[2,8],wai:8,wave:3,week:15,weekli:15,what:[1,16],when:[],which:[2,16],why:16,wisconsin:6,write:[0,10],xgboost:9,your:[1,9]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index e925d1209..e65587b6a 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -134,10 +134,11 @@ For the reading assignments we use the following abbreviations:
 ###  Week 43 October 25-29
 - Lab Wednesday: Work on project 2
 - Lecture Thursday:  Recurrent Neural Networks
-- Lecture Friday: Recurrent Neural Networks and time series
+- Lecture Friday: Recurrent Neural Networks and principal component analysis (PCA)
 - Reading recommendations:
   - See lecture notes for week 43 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - For RNNs, see Goodfellow et al chapter 10 and discussions in chapter 11 and 12 on practicalities and applications
+  - For PCA see lecture notes (jupyter book) chapter 11 and also chapter 12 on clustering. Geron's chapter 8 is also a good read.
 
 ###  Week 44 November 1-5
 - Lab Wednesday: Work on project 2