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Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40","<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","Exercises week 39","Exercises week 41","Exercises week 42","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","Project 2 on Machine Learning, deadline November 4 (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: Linear Regression and Statistical interpretations","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","Week 42 Constructing a Neural Network code with examples"],titleterms:{"0":40,"1":[1,16,17,18,19,23,26,32,33,39,40],"11":39,"14":40,"16":36,"2":[1,16,17,18,19,23,27,32,33,34,39,40],"2023":30,"2024":[26,37,38,39,40],"21":[],"23":37,"26":33,"27":37,"3":[1,16,17,23,32,33,39,40],"30":38,"34":[16,32],"35":[17,33],"36":[18,34],"37":[19,35],"38":[0,20,36],"39":[21,22,37],"4":[1,23,27,33,40],"40":[0,22,38],"41":[22,39],"42":[0,23,40],"5":[1,23],"6":23,"7":[23,26,39],"9":35,"case":[9,11,29,33,34,36,37],"class":[0,36,39],"do":[2,34,35,37,38,39,40],"final":[13,22,27,33,34,37,38,39,40],"float":39,"function":[0,1,2,7,8,9,11,12,13,14,22,26,27,29,32,33,34,35,36,37,38,39,40],"import":[6,22,25,32,33,34,38,39,40],"new":[5,34,35,39],A:[0,1,2,5,9,10,22,32,34,35,36,38,39,40],AND:[38,39],And:[22,32,33,34,36,37,38],But:[22,37,38],For:33,In:[30,39],Is:[39,40],Ising:7,OR:[38,39],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,24,32,33,34,35,36,37,38,39,40],To:[32,33],With:[5,34],about:[32,33],abov:[34,39,40],activ:[2,13,23,27,34,38,39,40],ad:[1,7,18,26,32,33,38,39,40],adaboost:11,adagrad:[14,22,37,38],adam:[14,22,37,38],adapt:[11,22,37,38],adjust:[2,40],advanc:[22,38],adversari:5,again:[0,4,10,36],ai:[26,32],aim:[9,10,18,19,20,21,22,23,32],aka:[32,33],al:[22,38],algebra:[25,32],algorithm:[10,11,12,13,22,27,33,37,38,39,40],algortithm:[14,36,37],all:[9,39,40],an:[1,5,11,32,39],analys:[6,33],analysi:[1,6,7,12,24,26,27,29,32,33,34,35,39],analyt:[1,17,18,22],analyz:[39,40],ani:[14,36,37],anoth:[10,34,35],appli:24,approach:[1,9,15,32,35,37,38],approxim:[13,39],architectur:[2,40],argument:[37,38],arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[3,14,22,37,38],automat:[14,22,37,38,39],avoid:[],b:[18,26,27,37],back:[2,12,13,39,40],background:[24,26,27,35],bag:11,base:[14,22,35,37,38],basic:[1,6,8,10,11,12,25,33,34,35,36,39],batch:[2,37,38,39,40],bay:[6,34,35],befor:12,bengio:[39,40],beta:[34,35],better:[9,38,39],bia:[7,26,35],bias:[39,40],binari:[2,40],bind:32,bird:11,boldsymbol:[33,34,35],book:[39,40],boost:11,bootstrap:[7,11,35],boston:1,breast:[2,40],brief:[32,35,36,37],bring:[13,39,40],build:[2,4,10,40],c:[26,27,32],calcul:33,can:[22,32,35,37,38,39],cancer:[0,2,8,10,12,36,40],cart:10,center:33,central:[14,24,29,35,36,37],chain:[13,39,40],challeng:36,chang:11,channel:32,chi:[1,32],choic:[39,40],choos:[2,40],cifar01:4,classic:12,classif:[0,2,10,11,27,36,40],classifi:[9,36],clip:[2,39,40],cluster:15,cnn:4,code:[1,2,3,6,10,12,13,14,15,22,27,32,33,34,35,36,37,38,39,40],collect:[2,4,40],come:36,commun:32,compact:[0,36,39,40],compar:[3,11],comparison:34,compet:[22,37,38],complet:[33,39,40],complex:[1,7,26,33],complic:[7,37,38,39],compon:12,comput:[10,37,38],computation:35,computerlab:32,con:10,concept:29,condit:[34,35,36,37],confid:35,conjug:[14,37],consider:[39,40],construct:[39,40],continu:38,contn:32,convex:[9,14,36,37],convolut:[4,13,38,39],correctli:[34,35],correl:[0,12,33,36],correspond:[36,37],cost:[0,2,11,33,34,35,36,37,39,40],count:39,cours:[24,31,32],covari:[6,12,29,33],cover:32,critic:27,cross:[0,7,26,35,36],custom:23,cython:32,d:[26,27],data:[0,1,2,4,7,8,10,12,16,17,23,24,26,29,32,33,34,36,39,40],dataset:[2,4,40],deadlin:[26,27,32],decai:[3,37,38],decis:[10,11],decomposit:[6,12,18,25,33],deep:[2,3,32,36,39,40],defin:[2,32,39,40],definit:[39,40],degre:[1,33],deliveri:[26,27],delta:35,dens:[1,32],deriv:[6,13,33,34,35,36,37,39,40],descent:[0,3,11,14,22,27,36,37,38],descript:26,design:33,detail:[4,32],develop:[2,40],diagon:12,differ:[9,27,37,38],different:22,differenti:[3,14,37,38,39],diffus:3,dimension:[3,4,9,26,33],directli:[37,38],disadvantag:10,discret:29,discuss:[0,36],distribut:[6,29,34,35],distrubut:35,doe:[33,34,38,39],domain:29,dot:37,down:[2,39,40],dropout:[2,39,40],e:[26,27],each:[23,36],economi:33,electron:[26,27],element:[1,29,32,37,38],elimin:25,elu:[39,40],energi:32,ensembl:11,entri:[39,40],entropi:[0,10,36],environ:[1,16,32],equat:[1,3,13,32,33,34,36,37,39,40],error:[1,11,32,33,35],essenti:32,estim:[34,35],et:[22,38],etc:32,euler:3,evalu:[2,27,39,40],exampl:[0,1,2,3,4,5,7,8,9,10,11,22,32,33,34,35,36,37,38,39,40],exercis:[0,1,7,16,17,18,19,20,21,22,23,32,33,39],expect:[19,29,34,35],expens:35,experi:29,explicit:[39,40],explod:[39,40],explor:[1,16,17,32],exponenti:3,express:[0,18,19,33,36,37,39,40],extend:[0,36,37,39],extrapol:5,extrem:[11,32],ey:11,f:[26,27],fall:30,famili:[2,32,33,39,40],famou:25,fantast:33,featur:[10,25,33],feed:[2,13,38,39,40],find:[35,37],fine:[2,39,40],first:[5,13,27,32,33,34,36,37,39,40],fit:[1,11,32,34],fix:33,fold:[35,36],forc:4,forest:11,format:[26,27,32],forward:[2,3,13,38,39,40],fourier:4,frank:[7,26,33],freedom:[1,33],frequent:33,frequentist:[1,32],fridai:[],from:[0,6,11,13,22,27,33,34,35,36,37,38,39,40],full:[3,40],funtion:[39,40],further:[4,6,33],g:26,gan:5,gate:[38,39,40],gaussian:25,gd:[14,22,37,38],gener:[5,10,32,39],geometr:[12,36,37],get:[22,38,39],gini:10,glorot:[39,40],good:[1,32],goodfellow:[22,38],grade:[30,32],gradient:[0,2,3,11,14,22,27,36,37,38,39,40],grid:36,group:36,growth:3,ha:24,hand:[39,40],ha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