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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\">3. </span>Solving Differential Equations  with Deep Learning","<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","<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:{"case":[5,6,8,15],"final":[5,10],"function":[0,1,4,5,6,8,9,10,15],"import":[0,13,15],And:5,Eye:8,ODE:2,The:[0,1,2,3,4,5,6,7,9,10,12,15],Useful:[0,12],Using:[2,5],activ:[1,10],actual:15,adaboost:8,adapt:8,adjust:1,again:7,aim:[6,7],algebra:13,algorithm:[4,7,8,9,10],algortithm:5,all:6,analysi:[9,12,15],ani:5,anoth:7,appli:12,approach:[5,6],approxim:10,architectur:1,arrai:[0,13],autocorrel:15,autograd:2,back:[1,9,10],bag:8,basic:[0,5,7,8,9,13],batch:1,befor:9,better:[4,6,15],bia:3,binari:1,binomi:15,bird:8,block:15,book:11,boost:8,bootstrap:[3,8,15],boston:0,breast:1,brief:5,bring:10,build:[1,7],calcul:15,cancer:[1,5,7,9],cart:7,central:[5,12,15],chain:10,chang:8,chi:0,choos:1,classic:9,classif:[1,7,8],classifi:6,clip:1,code:[0,1,2,5,7,9,10,15],collect:1,compar:[2,8],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:[1,5,8],cours:[12,17],covari:[4,9,15],cross:3,cumul:15,cython:0,data:[0,1,5,7,9,12,15],dataset:1,decai:2,decis:[7,8],decomposit:[4,9,13],deep:[1,2],defin:1,definit:15,degre:0,demonstr:15,dens:0,deriv:[5,10],descent:[2,5,8],develop:1,deviat:15,diagon:9,dice:15,differ:[5,6],differenti:2,diffus:2,dimension:[2,6],disadvantag:7,discret:15,disguis:15,distribut:15,doing:1,domain:15,down:1,dropout:1,economi:4,element:[0,15],elimin:13,ensembl:8,entropi:7,equat:[0,2,5,10],error:8,etc:0,euler:2,evalu:1,event:15,exampl:[0,1,2,5,6,7,8,15],expect:15,experi:15,exponenti:[2,15],express:5,extend:5,extrem:8,famili:1,famou:[0,15],fantast:4,featur:[0,7,13],feed:[1,10],fine:1,first:[5,10],fit:[0,8],forest:8,forward:[1,2,10],frank:0,freedom:0,fridai:5,from:[8,10],full:2,gaussian:[13,15],gener:[7,15],geometr:[5,9],gini:7,grade:16,gradient:[1,2,5,8],growth:2,handl:[0,13],has:12,hessian:5,hidden:2,homework:5,hous:0,how:15,hyperparamet:1,hyperplan:6,id3:7,idea:9,ideal:5,implement:[1,15],improv:1,increment:9,index:7,inform:16,input:2,instal:[0,12],instructor:16,interpret:[5,9],introduc:[4,9],introduct:[0,3,12,13],invers:13,iter:[5,8],its:15,jackknif:15,julia:0,jungl:8,jupyt:11,kera:1,kernel:[6,9],lagrangian:6,lasso:4,layer:[1,2,10],learn:[0,1,2,5,9,12],level:8,librari:[0,12],likelihood:5,limit:[1,5,15],linear:[0,6,13],link:[4,9,14,17],logist:5,loss:5,machin:[0,5,6,12],main:15,make:[7,8],mani:[8,10],materi:14,mathemat:6,matric:0,matrix:[0,1,5,9,10,13,15],matter:0,mean:15,meet:[0,8,15],mercer:6,mersenn:15,method:[3,5,7,8,15],mlp:10,model:[0,1,10],moment:15,moon:[6,7],more:[0,5,13],multilay:10,multipl:1,multipli:6,name:15,need:0,network:[1,2,5,10],neural:[1,2,10],newton:5,non:6,normal:[1,15],notat:10,now:[1,7],nuclear:0,nueral:5,numba:0,number:[0,2,15],numer:[2,15],numpi:[0,13],observ:15,obtain:9,one:[2,5,10],optim:[1,5,6,12],organ:0,oslo:17,other:[7,9,10,15],our:[0,5,9,15],outcom:12,output:[2,15],overarch:[0,6,7],overview:8,own:[8,9],packag:[0,13],panda:0,part:[5,12],partial:2,pass:1,pca:9,pdf:15,perceptron:10,perform:[1,7],period:15,perspect:1,poisson:[2,15],popul:2,pre:1,preprocess:0,prerequisit:12,princip:9,pro:7,probabl:15,problem:[1,2,5],procedur:7,process:1,program:[2,5],propag:[1,10],properti:15,pseudo:15,python:[0,7,12,13,15],quick:6,ran0:15,random:[8,9,15],raphson:5,read:7,recip:15,recurr:10,reduc:0,reformul:2,regress:[0,4,5,7,8],regular:[1,4],relev:17,relu:1,remind:[3,5,6],requir:[2,12],resampl:3,resourc:2,revisit:5,ridg:[4,5],rng:15,rule:10,sampl:[9,15],schedul:14,schemat:7,scheme:2,scikit:[0,1,5,9],select:15,sensit:5,septemb:5,set:[2,7,10],sgd:5,should:[1,15],simpl:[0,5,7,15],singl:8,singular:[4,9],situat:15,size:4,slightli:5,soft:6,softmax:1,softwar:0,solv:[2,5],some:[0,5],specifi:2,split:0,squar:8,standard:[5,15],state:0,statist:[3,12,15],steepest:[5,8],step:[3,5],stochast:[5,15],stop:5,supervis:1,support:6,svd:4,teach:14,teacher:16,techniqu:9,technolog:12,tensorflow:1,test:[0,1],textbook:17,than:5,theorem:[6,9,10,15],theori:15,three:15,togeth:10,top:1,toss:15,toward:9,tradeoff:3,train:[0,1],tree:[7,8],tune:1,two:[6,12],type:[2,10],uncorrel:15,understand:4,uniform:15,univers:[10,17],use:[1,15],used:5,using:[0,2,5,15],valid:3,valu:[4,9,15],variabl:[5,15],varianc:[3,15],variou:3,vector:[0,6,10,13],view:[0,8],visual:[1,7],wai:7,wave:2,what:[0,15],when:5,which:[1,15],why:15,wisconsin:5,write:9,xgboost:8,your:8}})