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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<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\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks"],titleterms:{"1":[0,15,16,17,18,23,28,29],"14":31,"2":[0,15,16,17,18,28,29],"2023":[23,26],"21":32,"23":32,"3":[0,15,16,28,29],"31":29,"34":[15,28],"35":[16,29],"36":[17,30],"37":[18,31],"38":[19,32],"39":[20,33],"4":[0,29],"40":34,"5":0,"7":30,"9":23,"case":[8,10,25,29,30,32,33],"class":32,"do":[1,28,30,31,33,34],"final":[12,29,30,33,34],"function":[0,1,6,7,8,10,11,12,13,23,25,28,29,30,31,32,33,34],"import":[5,22,28,29,30],"new":[4,30,31],A:[0,1,4,8,9,28,30,31,32],AND:34,And:[28,29,30,32,33,34],But:[33,34],In:26,Ising:6,OR:34,The:[0,1,2,3,5,6,7,8,9,11,12,17,21,28,29,30,31,32,33,34],To:[28,29],With:[4,30],about:[28,29],abov:30,activ:[1,12,30,34],ad:[0,6,17,23,28,29,34],adaboost:10,adagrad:[13,33,34],adam:[13,33,34],adapt:[10,33,34],adjust:1,adversari:4,again:[3,9,32],ai:28,aim:[8,9,17,18,19,20,28],aka:[28,29],algebra:[22,28],algorithm:[9,10,11,12,29,33,34],algortithm:[13,32,33],all:8,an:[0,4,10,28],analys:[5,29],analysi:[0,5,6,11,21,23,25,28,29,30,31],analyt:[0,16,17],ani:[13,32,33],anoth:[9,30,31],appli:21,approach:[0,8,14,28,31,33,34],approxim:12,architectur:1,argument:[33,34],arrai:[22,28],artifici:34,assist:26,assumpt:[30,31],august:29,autocorrel:25,autograd:[2,13,33,34],automat:[13,33,34],avoid:[33,34],b:[17,23,33,34],back:[1,11,12],background:[21,23,31],bag:10,base:[13,31,33,34],basic:[0,5,7,9,10,11,22,29,30,31,32],batch:[1,33,34],bay:[5,30,31],befor:11,beta:[30,31],better:[8,34],bia:[6,23,31],binari:1,bind:28,bird:10,boldsymbol:[29,30,31],boost:10,bootstrap:[6,10,31],boston:[0,29],breast:1,brief:[28,31,32,33],bring:12,build:[1,3,9],c:[23,28],calcul:29,can:[28,31,33,34],cancer:[1,7,9,11,32],cart:9,center:29,central:[13,21,25,31,32,33],chain:12,challeng:32,chang:10,channel:28,chi:[0,28],choos:1,cifar01:3,classic:11,classif:[1,9,10,32],classifi:[8,32],clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,28,29,30,31,32,33,34],collect:[1,3],come:32,commun:28,compact:32,compar:[2,10],comparison:30,compet:[33,34],complet:29,complex:[0,6,23,29],complic:[6,33,34],compon:11,comput:[9,33,34],computation:31,computerlab:28,con:9,concept:25,condit:[30,31,32,33],confid:31,conjug:[13,33],continu:34,contn:28,convex:[8,13,32,33],convolut:[3,12,34],correctli:[30,31],correl:[11,29,32],correspond:[32,33],cost:[1,10,29,30,31,32,33],cours:[21,27,28],covari:[5,11,25,29],cover:28,cross:[6,23,31,32],cython:28,d:23,data:[0,1,3,6,7,9,11,15,16,21,23,25,28,29,30,32],dataset:[1,3],deadlin:[23,28],decai:[2,33,34],decis:[9,10],decomposit:[5,11,17,22,29],deep:[1,2,32],defin:[1,28],degre:[0,29],deliveri:23,delta:31,dens:[0,28],deriv:[5,12,29,30,31,32,33],descent:[2,10,13,32,33,34],descript:23,design:29,detail:[3,28],develop:1,diagon:11,differ:[8,33,34],differenti:[2,13,33,34],diffus:2,dimension:[2,3,8,23,29],directli:[33,34],disadvantag:9,discret:25,discuss:32,distribut:[5,25,30,31],distrubut:[30,31],doe:[29,30,34],domain:25,dot:[33,34],down:1,dropout:1,e:23,each:32,economi:29,electron:23,element:[0,25,28,33,34],elimin:22,energi:28,ensembl:10,entropi:[9,32],environ:[0,15,28],equat:[0,2,12,28,29,30,32,33],error:[0,10,28,29,31],essenti:28,estim:[30,31],etc:28,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,28,29,30,31,32,33,34],exercis:[0,6,15,16,17,18,19,20,28,29,31],expect:[18,25,30,31],expens:31,experi:25,explor:[0,15,16,28],exponenti:2,express:[17,18,28,29,32,33],extend:[32,33],extrapol:4,extrem:[10,28],ey:10,f:23,fall:26,famili:[1,28,29],famou:22,fantast:29,featur:[9,22,29],feed:[1,12,34],find:[31,33,34],fine:1,first:[4,12,28,29,30,32,33],fit:[0,10,28,30],fix:29,fold:[31,32],forc:3,forest:10,format:[23,28],forward:[1,2,12,34],fourier:3,frank:[6,23,29],freedom:[0,29],frequent:29,frequentist:[0,28],fridai:32,from:[5,10,12,29,30,31,32,33,34],full:2,further:[3,5,29],g:23,gan:4,gate:34,gaussian:22,gd:[13,33,34],gener:[4,9,28],geometr:[11,32,33],gini:9,good:[0,28],grade:[26,28],gradient:[1,2,10,13,32,33,34],grid:32,group:32,growth:2,ha:21,handl:[22,28,29],happen:[30,31],have:28,heard:28,hessian:[29,32,33],hidden:2,histogram:31,homework:[32,33],hous:[0,29],how:32,hyperbol:34,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ideal:[32,33],ident:[30,31],identifi:31,ii:28,iid:[30,31],illustr:[30,34],implement:1,implic:[5,29],improv:[1,33],includ:[13,32,33,34],increment:11,independ:[30,31],index:9,inform:26,input:2,instal:[21,23,28],instructor:26,intercept:29,interpret:[5,11,28,29,30,31,32,33],interv:31,introduc:[11,13,29,33,34],introduct:[0,6,21,22,23,28,34],invers:[5,22,30],invert:29,iter:[10,33],its:29,jacobian:29,jax:[13,33,34],job:34,julia:28,jungl:10,k:[31,32],kera:[1,3],kernel:[8,11],lagrangian:8,lambda:32,lasso:[5,6,23,29,30,31,32],last:[29,31,32,34],later:[5,29],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,21,23,28,29,30,31,32,33,34],least:[5,6,18,23,28,29,30],lectur:[28,29,30,31,32],level:10,librari:[21,28],likelihood:[7,30,31,32],limit:[1,13,25,31,32,33,34],linear:[0,8,13,22,28,29,30,32],link:[5,11,24,27,29,30,31],literatur:23,logist:[7,28,32,33,34],loop:[33,34],loss:[29,32,33],lu:22,machin:[0,8,13,21,23,28,32,33],made:[30,31],mai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