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th:28,zfill:4,zip:[2,4,6],zm_h:[0,27],zn:0,zone:0,zoom:27,zx:[21,27],zy:[21,27],zz:[21,27]},titles:["<span 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","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 7 (midnight), 2024","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: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization"],titleterms:{"1":[0,15,16,17,18,22,27,28],"16":31,"2":[0,15,16,17,18,27,28,29],"2023":25,"2024":22,"21":[],"23":[],"26":28,"3":[0,15,16,27,28],"34":[15,27],"35":[16,28],"36":[17,29],"37":[18,30],"38":[19,31],"4":[0,28],"5":0,"7":22,"9":30,"case":[8,10,24,28,29,31],"class":31,"do":[1,29,30],"final":[12,28,29],"function":[0,1,6,7,8,10,11,12,13,22,24,27,28,29,30,31],"import":[5,21,27,28,29],"new":[4,29,30],A:[0,1,4,8,9,27,29,30,31],And:[27,28,29,31],For:28,In:25,Ising:6,The:[0,1,2,3,5,6,7,8,9,11,12,17,20,27,28,29,30,31],To:[27,28],With:[4,29],about:[27,28],abov:29,activ:[1,12,29],ad:[0,6,17,22,27,28],adaboost:10,adagrad:13,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9,31],ai:[22,27],aim:[8,9,17,18,19,27],aka:[27,28],algebra:[21,27],algorithm:[9,10,11,12,28],algortithm:[13,31],all:8,an:[0,4,10,27],analys:[5,28],analysi:[0,5,6,11,20,22,24,27,28,29,30],analyt:[0,16,17],ani:[13,31],anoth:[9,29,30],appli:20,approach:[0,8,14,27,30],approxim:12,architectur:1,arrai:[21,27],assist:25,assumpt:[29,30],august:28,autocorrel:24,autograd:[2,13],automat:13,b:[17,22],back:[1,11,12],background:[20,22,30],bag:10,base:[13,30],basic:[0,5,7,9,10,11,21,28,29,30,31],batch:1,bay:[5,29,30],befor:11,beta:[29,30],better:8,bia:[6,22,30],binari:1,bind:27,bird:10,boldsymbol:[28,29,30],boost:10,bootstrap:[6,10,30],boston:0,breast:1,brief:[27,30,31],bring:12,build:[1,3,9],c:[22,27],calcul:28,can:[27,30],cancer:[1,7,9,11,31],cart:9,center:28,central:[13,20,24,30,31],chain:12,challeng:31,chang:10,channel:27,chi:[0,27],choos:1,cifar01:3,classic:11,classif:[1,9,10,31],classifi:[8,31],clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,27,28,29,30,31],collect:[1,3],come:31,commun:27,compact:31,compar:[2,10],comparison:29,complet:28,complex:[0,6,22,28],complic:6,compon:11,comput:9,computation:30,computerlab:27,con:9,concept:24,condit:[29,30,31],confid:30,conjug:13,contn:27,convex:[8,13,31],convolut:[3,12],correctli:[29,30],correl:[11,28,31],correspond:31,cost:[1,10,28,29,30,31],cours:[20,26,27],covari:[5,11,24,28],cover:27,cross:[6,22,30,31],cython:27,d:22,data:[0,1,3,6,7,9,11,15,16,20,22,24,27,28,29,31],dataset:[1,3],deadlin:[22,27],decai:2,decis:[9,10],decomposit:[5,11,17,21,28],deep:[1,2,27,31],defin:[1,27],degre:[0,28],deliveri:22,delta:30,dens:[0,27],deriv:[5,12,28,29,30,31],descent:[2,10,13,31],descript:22,design:28,detail:[3,27],develop:1,diagon:11,differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8,22,28],disadvantag:9,discret:24,discuss:31,distribut:[5,24,29,30],distrubut:30,doe:[28,29],domain:24,down:1,dropout:1,e:22,each:31,economi:28,electron:22,element:[0,24,27],elimin:21,energi:27,ensembl:10,entropi:[9,31],environ:[0,15,27],equat:[0,2,12,27,28,29,31],error:[0,10,27,28,30],essenti:27,estim:[29,30],etc:27,euler:2,evalu:1,exampl:[0,1,2,3,4,6,7,8,9,10,27,28,29,30,31],exercis:[0,6,15,16,17,18,19,27,28],expect:[18,24,29,30],expens:30,experi:24,explor:[0,15,16,27],exponenti:2,express:[17,18,28,31],extend:31,extrapol:4,extrem:[10,27],ey:10,f:22,fall:25,famili:[1,27,28],famou:21,fantast:28,featur:[9,21,28],feed:[1,12],find:30,fine:1,first:[4,12,27,28,29,31],fit:[0,10,27,29],fix:28,fold:[30,31],forc:3,forest:10,format:[22,27],forward:[1,2,12],fourier:3,frank:[6,22,28],freedom:[0,28],frequent:28,frequentist:[0,27],fridai:[],from:[5,10,12,28,29,30,31],full:2,further:[3,5,28],g:22,gan:4,gaussian:21,gd:13,gener:[4,9,27],geometr:[11,31],gini:9,good:[0,27],grade:[25,27],gradient:[1,2,10,13,31],grid:31,group:31,growth:2,ha:20,handl:[21,27,28],happen:[29,30],hessian:[28,31],hidden:2,histogram:30,homework:31,hous:0,how:31,hyperparamet:1,hyperplan:8,i:1,id3:9,idea:11,ideal:31,ident:[29,30],identifi:30,ii:27,iid:[29,30],illustr:29,implement:1,implic:[5,28],improv:1,includ:[13,31],increment:11,independ:[29,30],index:9,inform:25,input:2,instal:[20,22,27],instructor:25,intercept:28,interpret:[5,11,27,28,29,30,31],interv:30,introduc:[11,13,28],introduct:[0,6,20,21,22,27],invers:[5,21,29],invert:28,iter:10,its:28,jacobian:28,jax:13,julia:27,jungl:10,k:[30,31],kera:[1,3],kernel:[8,11],lab:[30,31],lagrangian:8,lambda:31,lasso:[5,6,22,28,29,30,31],last:[28,31],later:[5,28],layer:[1,2,3,12],learn:[0,1,2,11,13,14,15,16,20,22,27,28,29,30,31],least:[5,6,18,22,27,28,29],lectur:[27,28,29,30,31],level:10,librari:[20,27],likelihood:[7,29,30,31],limit:[1,13,24,30,31],linear:[0,8,13,21,27,28,29,31],link:[5,11,23,26,28,29,30],literatur:22,logist:[7,27,31],loss:[28,31],lu:21,machin:[0,8,13,20,22,27,31],made:[29,30],main:[24,27],make:[0,9,10,15,16,27,28],mani:[10,12],manipul:28,margin:[29,30],mass:27,materi:[22,23,27,28,29,30,31],math:[5,28],mathemat:[3,5,8,28],matric:[5,21,27,29],matrix:[1,5,11,12,21,27,28,29,31],matter:[0,27],max:28,maximum:[29,30,31],mean:[0,28,29,31],measur:31,meet:[5,10,24,27,28],mercer:8,method:[6,9,10,13,22,27,30,31],midnight:22,min:28,minim:[27,31],ml:27,mle:[29,30],mlp:12,mnist:[3,4],model:[0,1,4,6,12,27],momentum:13,mondai:[28,29,30,31],moon:[8,9],more:[3,6,21,22,27,28,29,30,31],multilay:12,multipl:[1,3],multipli:8,need:[22,27],net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