From e8d09a176724123124f9bca02613ab1ddeef1bda Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 30 Sep 2021 17:18:10 +0200 Subject: [PATCH] updated lecture schedule --- README.md | 1 + .../_build/.doctrees/environment.pickle | Bin 178272 -> 178278 bytes .../_build/.doctrees/schedule.doctree | Bin 65518 -> 66561 bytes .../_build/html/_sources/schedule.md | 1 + doc/LectureNotes/_build/html/schedule.html | 6 +++++- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/schedule.md | 1 + 7 files changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 697ea288c..6521b4b5b 100644 --- a/README.md +++ b/README.md @@ -320,6 +320,7 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics) ### Week 39 September 27- October 1 - Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods + - Video of Lecture at 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https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember30.mp4?vrtx=view-as-webpage - Lecture Friday: Gradient methods - Reading recommendations: diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html index 0f4a56d6d..720c32e81 100644 --- a/doc/LectureNotes/_build/html/schedule.html +++ b/doc/LectureNotes/_build/html/schedule.html @@ -513,7 +513,11 @@

Week 39 September 27- October 1¶

  • Lab Wednesday: Work on Project 1

    • -

  • Lecture Thursday: Gradient Optimization methods

    • +

  • Lecture Thursday: Gradient Optimization methods

    + +

  • Lecture Friday: Gradient methods

  • Reading recommendations:

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Clustering Analysis","3. Linear Regression","14. Building a Feed Forward Neural Network","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. 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","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. 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 75baaeae3..68c0ffd11 100644 --- a/doc/LectureNotes/schedule.md +++ b/doc/LectureNotes/schedule.md @@ -90,6 +90,7 @@ For the reading assignments we use the following abbreviations: ### Week 39 September 27- October 1 - Lab Wednesday: Work on Project 1 - Lecture Thursday: Gradient Optimization methods + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember30.mp4?vrtx=view-as-webpage - Lecture Friday: Gradient methods - Reading recommendations: