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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","Exercises week 41","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","Project 2 on Machine Learning, deadline November 13 (Midnight)","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","Week 41 Neural networks and constructing a neural network code"],titleterms:{"1":[0,15,16,17,18,24,30,31],"12":37,"13":25,"14":33,"2":[0,15,16,17,18,25,30,31],"2023":[24,28],"21":34,"23":34,"3":[0,15,16,30,31],"31":31,"34":[15,30],"35":[16,31],"36":[17,32],"37":[18,33],"38":[19,34],"39":[20,21,35],"4":[0,31],"40":[21,36],"41":[21,37],"5":0,"7":32,"9":24,"case":[8,10,27,31,32,34,35],"class":34,"do":[1,30,32,33,35,36,37],"final":[12,21,25,31,32,35,36,37],"function":[0,1,6,7,8,10,11,12,13,21,24,25,27,30,31,32,33,34,35,36,37],"import":[5,21,23,30,31,32],"new":[4,32,33],A:[0,1,4,8,9,21,30,32,33,34],AND:[36,37],And:[21,30,31,32,34,35,36],But:[21,35,36],In:28,Ising:6,OR:[36,37],The:[0,1,2,3,5,6,7,8,9,11,12,17,22,30,31,32,33,34,35,36,37],To:[30,31],With:[4,32],about:[30,31],abov:32,activ:[1,12,25,32,36,37],ad:[0,6,17,24,30,31,36,37],adaboost:10,adagrad:[13,21,35,36],adam:[13,21,35,36],adapt:[10,21,35,36],adjust:[1,37],advanc:21,adversari:4,again:[3,9,34],ai:30,aim:[8,9,17,18,19,20,21,30],aka:[30,31],al:21,algebra:[23,30],algorithm:[9,10,11,12,21,25,31,35,36,37],algortithm:[13,34,35],all:8,an:[0,4,10,30],analys:[5,31],analysi:[0,5,6,11,22,24,25,27,30,31,32,33],analyt:[0,16,17,21],ani:[13,34,35],anoth:[9,32,33],appli:22,approach:[0,8,14,30,33,35,36],approxim:[12,37],architectur:[1,37],argument:[35,36],arrai:[23,30],artifici:[36,37],assist:28,assumpt:[32,33],august:31,autocorrel:27,autograd:[2,13,21,35,36],automat:[13,21,35,36],avoid:[35,36],b:[17,24,25,35,36],back:[1,11,12,37],background:[22,24,25,33],bag:10,base:[13,21,33,35,36],basic:[0,5,7,9,10,11,23,31,32,33,34],batch:[1,35,36],bay:[5,32,33],befor:11,beta:[32,33],better:[8,36,37],bia:[6,24,33],bias:37,binari:[1,37],bind:30,bird:10,boldsymbol:[31,32,33],boost:10,bootstrap:[6,10,33],boston:[0,31],breast:1,brief:[30,33,34,35],bring:[12,37],build:[1,3,9],c:[24,25,30],calcul:31,can:[21,30,33,35,36],cancer:[1,7,9,11,34],cart:9,center:31,central:[13,22,27,33,34,35],chain:[12,37],challeng:34,chang:10,channel:30,chi:[0,30],choos:[1,37],cifar01:3,classic:11,classif:[1,9,10,25,34,37],classifi:[8,34],clip:1,cluster:14,cnn:3,code:[0,1,2,5,9,11,12,13,14,21,25,30,31,32,33,34,35,36,37],collect:[1,3,37],come:34,commun:30,compact:34,compar:[2,10],comparison:32,compet:[21,35,36],complet:31,complex:[0,6,24,31],complic:[6,35,36],compon:11,comput:[9,35,36],computation:33,computerlab:30,con:9,concept:27,condit:[32,33,34,35],confid:33,conjug:[13,35],construct:37,continu:36,contn:30,convex:[8,13,34,35],convolut:[3,12,36,37],correctli:[32,33],correl:[11,31,34],correspond:[34,35],cost:[1,10,31,32,33,34,35,37],cours:[22,29,30],covari:[5,11,27,31],cover:30,critic:25,cross:[6,24,33,34],cython:30,d:[24,25],data:[0,1,3,6,7,9,11,15,16,22,24,27,30,31,32,34,37],dataset:[1,3,37],deadlin:[24,25,30],decai:[2,35,36],decis:[9,10],decomposit:[5,11,17,23,31],deep:[1,2,34],defin:[1,30,37],definit:37,degre:[0,31],deliveri:[24,25],delta:33,dens:[0,30],deriv:[5,12,31,32,33,34,35,37],descent:[2,10,13,21,25,34,35,36],descript:24,design:31,detail:[3,30],develop:[1,37],diagon:11,differ:[8,25,35,36],different:21,differenti:[2,13,35,36],diffus:2,dimension:[2,3,8,24,31],directli:[35,36],disadvantag:9,discret:27,discuss:34,distribut:[5,27,32,33],distrubut:[32,33],doe:[31,32,36,37],domain:27,dot:[35,36],down:1,dropout:1,e:[24,25],each:34,economi:31,electron:[24,25],element:[0,27,30,35,36],elimin:23,energi:30,ensembl:10,entropi:[9,34],environ:[0,15,30],equat:[0,2,12,30,31,32,34,35,37],error:[0,10,30,31,33],essenti:30,estim:[32,33],et:21,etc:30,euler:2,evalu:[1,25,37],exampl:[0,1,2,3,4,6,7,8,9,10,21,30,31,32,33,34,35,36,37],exercis:[0,6,15,16,17,18,19,20,21,30,31,33],expect:[18,27,32,33],expens:33,experi:27,explor:[0,15,16,30],exponenti:2,express:[17,18,30,31,34,35],extend:[34,35],extrapol:4,extrem:[10,30],ey:10,f:[24,25],fall:28,famili:[1,30,31],famou:23,fantast:31,featur:[9,23,31],feed:[1,12,36,37],find:[33,35,36],fine:1,first:[4,12,25,30,31,32,34,35,37],fit:[0,10,30,32],fix:31,fold:[33,34],forc:3,forest:10,format:[24,25,30],forward:[1,2,12,36,37],fourier:3,frank:[6,24,31],freedom:[0,31],frequent:31,frequentist:[0,30],fridai:34,from:[5,10,12,21,25,31,32,33,34,35,36,37],full:[2,37],further:[3,5,31],g:24,gan:4,gate:[36,37],gaussian:23,gd:[13,21,35,36],gener:[4,9,30],geometr:[11,34,35],get:21,gini:9,good:[0,30],goodfellow:21,grade:[28,30],gradient:[1,2,10,13,21,25,34,35,36],grid:34,group:34,growth:2,ha:22,handl:[23,30,31],happen:[32,33],have:30,heard:30,hessian:[31,34,35],hidden:2,histogram:33,homework:[34,35],hous:[0,31],how:34,hyperbol:[36,37],hyperparamet:[1,37],hyperplan:8,i:1,id3:9,idea:11,ideal:[34,35],ident:[32,33],identifi:33,ii:30,iid:[32,33],illustr:[32,36,37],implement:[1,37],implic:[5,31],improv:[1,35,37],includ:[13,21,34,35,36],increment:11,independ:[32,33],index:9,inform:28,input:2,instal:[22,24,30],instructor:28,intercept:31,interpret:[5,11,30,31,32,33,34,35],interv:33,introduc:[11,13,21,31,35,36],introduct:[0,6,22,23,24,25,30,36,37],invers:[5,23,32],invert:31,iter:[10,35],its:31,jacobian:31,jax:[13,21,35,36],job:[36,37],julia:30,jungl:10,k:[33,34],kera:[1,3],ke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