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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">13. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">14. </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\">12. </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:{"1":14,"10":14,"11":14,"12":14,"13":14,"15":14,"17":14,"18":14,"19":14,"2":0,"20":14,"2021":16,"22":14,"23":14,"24":14,"25":14,"26":14,"27":14,"29":14,"3":14,"30":14,"34":14,"35":14,"36":14,"37":14,"38":14,"39":14,"4":14,"40":14,"41":14,"4155":12,"42":14,"43":14,"44":14,"45":14,"46":14,"47":14,"5":14,"6":14,"8":14,"case":[6,8,15],"do":1,"final":10,"function":[0,1,4,5,6,8,9,10,11,15],"import":[3,13,15],A:[0,1,6,7],Ising:4,The:[0,1,2,3,4,5,6,7,9,10,12,15],activ:[1,10],actual:15,ad:4,adaboost:8,adam:11,adapt:8,adjust:1,again:7,aim:[6,7],algebra:13,algorithm:[7,8,9,10],algortithm:11,all:6,an:[0,8],analys:3,analysi:[0,3,4,9,12,15],ani:11,anoth:7,appli:12,approach:[0,6],approxim:10,architectur:1,arrai:13,assist:16,august:14,autocorrel:15,autograd:2,automat:11,back:[1,9,10],background:12,bag:8,base:11,basic:[0,3,5,7,8,9,13],batch:1,bay:3,befor:9,better:[6,15],bia:4,binari:1,binomi:15,bird:8,block:15,boost:8,bootstrap:[4,8,15],boston:0,breast:1,bring:10,build:[1,7],calcul:15,cancer:[1,5,7,9],cart:7,central:[11,12,15],chain:10,chang:8,chi:0,choos:1,classic:9,classif:[1,7,8],classifi:6,clip:1,code:[0,1,2,3,7,9,10,15],collect:1,compar:[2,8],complex:[0,4],complic:4,compon:9,comput:[7,15],con:7,concept:15,conjug:11,continu:15,convex:[6,11],convolut:10,correl:[9,15],cost:[1,8],cours:[12,17],covari:[3,9,15],cross:4,cumul:15,data:[0,1,4,5,7,9,12,15],dataset:1,decai:2,decis:[7,8],decomposit:[3,9,13],deep:[1,2],defin:1,definit:15,degre:0,demonstr:15,dens:0,deriv:[3,10],descent:[2,8,11],develop:1,deviat:15,diagon:9,dice:15,differ:6,differenti:[2,11],diffus:2,dimension:[2,6],disadvantag:7,discret:15,disguis:15,distribut:[3,15],domain:15,down:1,dropout:1,element:[0,15],elimin:13,ensembl:8,entropi:7,environ:0,equat:[0,2,10],error:[0,8],euler:2,evalu:1,event:15,exampl:[0,1,2,4,5,6,7,8,15],exercis:[0,4],expect:15,experi:15,explor:0,exponenti:[2,15],extrem:8,ey:8,fall:16,famili:1,famou:15,featur:[7,13],feed:[1,10],fine:1,first:10,fit:[0,8],forest:8,forward:[1,2,10],frank:4,freedom:0,frequentist:0,from:[3,8,10],full:2,further:3,fy:12,gaussian:[13,15],gd:11,gener:[7,15],geometr:9,gini:7,good:0,grade:16,gradient:[1,2,8,11],growth:2,ha:12,handl:13,hidden:2,hous:0,how:15,hyperparamet:1,hyperplan:6,i:[1,15],id3:7,idea:9,implement:[1,15],implic:3,improv:1,increment:9,index:7,inform:16,input:2,instal:12,instructor:16,interpret:[3,9],introduc:9,introduct:[0,4,12,13],invers:[3,13],iter:8,its:15,jackknif:15,jungl:8,kera:1,kernel:[6,9],lagrangian:6,lasso:[3,4],later:3,layer:[1,2,10],learn:[0,1,2,9,11,12],least:[3,4],level:8,librari:12,likelihood:5,limit:[1,11,15],linear:[0,6,11,13],link:[3,9,14,17],logist:5,lu:13,machin:[0,6,11,12],main:15,make:[0,7,8],mani:[8,10],materi:14,math:3,mathemat:[3,6],matric:3,matrix:[1,3,9,10,13,15],matter:0,mean:[0,15],meet:[3,8,15],mercer:6,mersenn:15,method:[4,7,8,11,15],mlp:10,model:[0,1,4,10],moment:15,momentum:11,moon:[6,7],more:[4,13],multilay:10,multipl:1,multipli:6,name:15,network:[1,2,5,10],neural:[1,2,10],non:6,normal:[0,1,15],norwai:12,notat:10,novemb:14,now:[1,7],nuclear:0,nueral:5,number:[0,2,15],numer:[2,15],numpi:13,observ:15,obtain:9,octob:14,od:2,off:4,ol:[3,4],one:[2,10],oper:13,optim:[1,6,11,12],ordinari:[3,4],organ:0,oslo:[12,17],other:[7,9,10,13,15],our:[0,3,9,11,15],outcom:12,output:[2,15],overarch:[0,6,7],overview:8,own:[0,8,9],packag:13,part:[11,12],partial:2,pass:1,pca:9,pdf:15,perceptron:10,perform:[1,7],period:15,perspect:1,poisson:[2,15],popul:2,practic:11,pre:1,prerequisit:12,princip:9,pro:7,probabl:[3,15],problem:[1,2,11],procedur:7,process:1,program:[2,11],project:4,prop:11,propag:[1,10],properti:[3,15],pseudo:15,python:[0,7,12,13,15],quick:6,ran0:15,random:[8,9,15],read:7,real:4,recip:15,recurr:10,reduc:0,reformul:2,regress:[0,3,4,5,7,8,11],regular:1,relev:17,relu:1,remind:[4,6],requir:[2,12],resampl:4,rescal:4,resourc:2,revisit:11,ridg:[3,4],rm:11,rng:15,rule:10,s:[6,8,15],sampl:[9,15],schedul:14,schemat:7,scheme:2,scikit:[0,1,9],select:15,semest:16,septemb:14,set:[0,2,7,10],should:[1,15],simpl:[0,7,11,15],singl:8,singular:[3,9],situat:15,soft:6,softmax:1,solv:2,solver:11,some:11,specifi:2,split:0,squar:[0,3,4,8],standard:[11,15],state:0,statist:[3,4,12,15],steepest:[8,11],stk3155:12,stochast:[11,15],supervis:1,support:6,svd:3,teach:[14,16],teacher:16,techniqu:[4,9],technolog:12,tensorflow:1,test:[0,1],textbook:17,theorem:[3,6,9,10,15],theori:15,three:15,tip:11,togeth:10,top:1,toss:15,toward:9,trade:4,tradeoff:4,train:[0,1],tree:[7,8],tune:1,two:[6,12],type:[2,10],uncorrel:15,uniform:15,univers:[10,12,17],up:[0,2,7,10],us:[0,1,2,5,11,12,15],valid:4,valu:[3,9,15],variabl:15,varianc:[4,15],variou:0,vector:[6,10,13],view:[0,8],visual:[1,7],wai:7,wave:2,week:14,weekli:14,what:[0,15],which:[1,15],why:15,wisconsin:5,write:9,xgboost:8,your:[0,8]}}) 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