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class=\"section-number\">11. </span>Clustering Analysis","<span class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">13. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">4. </span>Resampling Methods","<span class=\"section-number\">5. </span>Ridge and Lasso Regression","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">7. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">8. </span>Decision trees, overarching aims","<span class=\"section-number\">9. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">10. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">12. </span>Neural networks","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":16,"4155":12,"case":[5,6,8,15],"final":[5,10],"function":[1,2,4,5,6,8,9,10,15],"import":[13,15],And:5,Eye:8,FYS:12,The:[0,1,2,3,4,5,6,7,9,10,12,15],Useful:12,Using:5,activ:[2,10],actual:15,adaboost:8,adapt:8,adjust:2,again:7,aim:[6,7],algebra:13,algorithm:[0,4,7,8,9,10],algortithm:5,all:6,analysi:[0,1,9,12,15],ani:5,anoth:7,appli:12,approach:[1,5,6],approxim:10,architectur:2,arrai:13,assist:16,august:14,autocorrel:15,back:[2,9,10],background:12,bag:8,basic:[0,1,5,7,8,9,13],batch:2,befor:9,better:[4,6,15],bia:3,binari:2,binomi:15,bird:8,block:15,book:11,boost:8,bootstrap:[3,8,15],boston:1,breast:2,brief:5,bring:10,build:[2,7],calcul:15,cancer:[2,5,7,9],cart:7,central:[5,12,15],chain:10,chang:8,chi:1,choos:2,classic:9,classif:[2,7,8],classifi:6,clip:2,cluster:0,code:[0,1,2,5,7,9,10,15],collect:2,compar:8,compon:9,comput:[5,7,15],con:7,concept:15,condit:5,conjug:5,content:11,continu:15,convex:[5,6],convolut:10,correl:[4,9,15],correspond:5,cost:[2,5,8],cours:[12,17],covari:[4,9,15],cross:3,cumul:15,cython:[],data:[1,2,5,7,9,12,15],dataset:2,decemb:14,decis:[7,8],decomposit:[4,9,13],deep:2,defin:2,definit:15,degre:1,demonstr:15,dens:1,deriv:[5,10],descent:[5,8],develop:2,deviat:15,diagon:9,dice:15,differ:[5,6],dimension:6,disadvantag:7,discret:15,disguis:15,distribut:15,doing:2,domain:15,down:2,dropout:2,economi:4,element:[1,15],elimin:13,ensembl:8,entropi:7,environ:1,equat:[1,5,10],error:8,etc:[],evalu:2,event:15,exampl:[1,2,5,6,7,8,15],exercis:1,expect:15,experi:15,explor:1,exponenti:15,express:5,extend:5,extrem:8,fall:16,famili:2,famou:15,fantast:4,featur:[7,13],feed:[2,10],fine:2,first:[5,10],fit:[1,8],forest:8,forward:[2,10],frank:1,freedom:1,frequentist:1,fridai:5,from:[8,10],gaussian:[13,15],gener:[7,15],geometr:[5,9],gini:7,good:1,grade:16,gradient:[2,5,8],handl:13,has:12,hessian:5,homework:5,hous:1,how:15,hyperparamet:2,hyperplan:6,id3:7,idea:[0,9],ideal:5,implement:[2,15],improv:2,increment:9,index:7,inform:16,instal:12,instructor:16,interpret:[5,9],introduc:[4,9],introduct:[1,3,12,13],invers:13,iter:[5,8],its:15,jackknif:15,julia:[],jungl:8,jupyt:11,kera:2,kernel:[6,9],lagrangian:6,lasso:4,layer:[2,10],learn:[1,2,5,9,12],level:8,librari:12,likelihood:5,limit:[2,5,15],linear:[1,6,13],link:[4,9,14,17],logist:5,loss:5,machin:[1,5,6,12],main:15,make:[1,7,8],mani:[8,10],materi:14,mathemat:6,matric:[],matrix:[2,5,9,10,13,15],matter:1,mean:[0,15],meet:[8,15],mercer:6,mersenn:15,method:[3,5,7,8,15],mlp:10,model:[1,2,10],moment:15,moon:[6,7],more:[0,5,13],multilay:10,multipl:2,multipli:6,name:15,need:[],network:[2,5,10],neural:[2,10],newton:5,non:6,normal:[1,2,15],norwai:12,notat:10,novemb:14,now:[2,7],nuclear:1,nueral:5,numba:[],number:[1,15],numer:15,numpi:13,numpython:0,observ:15,obtain:9,octob:14,one:[5,10],optim:[2,5,6,12],organ:1,oslo:[12,17],other:[7,9,10,15],our:[0,1,5,9,15],outcom:12,output:15,overarch:[1,6,7],overview:8,own:[0,1,8,9],packag:13,panda:[],part:[5,12],pass:2,pca:9,pdf:15,perceptron:10,perform:[2,7],period:15,perspect:2,poisson:15,pre:2,preprocess:1,prerequisit:12,princip:9,pro:7,probabl:15,problem:[2,5],procedur:7,process:2,program:5,propag:[2,10],properti:15,pseudo:15,python:[0,1,7,12,13,15],quick:6,ran0:15,random:[8,9,15],raphson:5,read:7,recip:15,recurr:10,reduc:1,regress:[1,4,5,7,8],regular:[2,4],relev:17,relu:2,remind:[3,5,6],requir:12,resampl:3,revisit:5,ridg:[4,5],rng:15,rule:10,sampl:[9,15],schedul:14,schemat:7,scikit:[1,2,5,9],select:15,semest:16,sensit:5,septemb:[5,14],set:[1,7,10],sgd:5,should:[2,15],simpl:[1,5,7,15],singl:8,singular:[4,9],situat:15,size:4,slightli:5,soft:6,softmax:2,softwar:[],solv:5,some:5,split:1,squar:8,standard:[5,15],state:1,statist:[3,12,15],steepest:[5,8],step:[3,5],stk3155:12,stochast:[5,15],stop:5,supervis:2,support:6,svd:4,teach:[14,16],teacher:16,techniqu:9,technolog:12,tensorflow:2,test:[1,2],textbook:17,than:5,theorem:[6,9,10,15],theori:15,three:15,togeth:10,top:2,toss:15,toward:[0,9],tradeoff:3,train:[1,2],tree:[7,8],tune:2,two:[6,12],type:10,uncorrel:15,understand:4,uniform:15,univers:[10,12,17],use:[2,15],used:5,using:[1,5,15],valid:3,valu:[4,9,15],variabl:[5,15],varianc:[3,15],variou:[1,3],vector:[6,10,13],view:[1,8],visual:[2,7],wai:7,week:14,weekli:14,what:[1,15],when:5,which:[2,15],why:15,wisconsin:5,write:[0,9],xgboost:8,your:[1,8]}})