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