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Morten Hjorth-Jensen 23145088bf udpate book
2023-10-30 10:33:26 +01:00

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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","Exercises week 42","Exercises weeks 43 and 44","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","Week 42 Constructing a Neural Network code with introduction to Tensor flow","Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations","Week 44, Convolutional Neural Networks (CNN)"],titleterms:{"1":[0,15,16,17,18,26,32,33],"12":39,"13":27,"14":35,"19":40,"2":[0,15,16,17,18,27,32,33,42],"2023":[26,30],"21":36,"23":36,"26":41,"3":[0,15,16,32,33],"31":33,"34":[15,32],"35":[16,33],"36":[17,34],"37":[18,35],"38":[19,36],"39":[20,21,37],"3d":42,"4":[0,33],"40":[21,38],"41":[21,39],"42":[22,40],"43":[23,41],"44":[23,41,42],"5":0,"7":34,"9":26,"case":[8,10,29,33,34,36,37],"class":36,"do":[1,32,34,35,37,38,39,40,41],"final":[12,21,27,33,34,37,38,39,41,42],"function":[0,1,6,7,8,10,11,12,13,21,23,26,27,29,32,33,34,35,36,37,38,39,40,41,42],"import":[5,21,25,32,33,34,42],"new":[4,34,35],A:[0,1,4,8,9,21,32,34,35,36,40,41,42],AND:[23,38,39,40,41],And:[21,32,33,34,36,37,38],But:[21,37,38],In:30,Is:[40,41],Ising:6,No:42,OR:[38,39,40,41],The:[0,1,2,3,5,6,7,8,9,11,12,17,23,24,32,33,34,35,36,37,38,39,40,41,42],To:[32,33],With:[4,34],about:[32,33],abov:[23,34,41,42],activ:[1,12,23,27,34,38,39,40,41,42],ad:[0,6,17,26,32,33,38,39],adaboost:10,adagrad:[13,21,37,38],adam:[13,21,37,38],adapt:[10,21,37,38],add:42,addit:42,adjust:[1,39,40,41],advanc:21,adversari:4,again:[3,9,36,42],ai:32,aim:[8,9,17,18,19,20,21,22,23,32],aka:[32,33],al:21,algebra:[25,32],algorithm:[9,10,11,12,21,27,33,37,38,39,40],algortithm:[13,36,37],all:8,an:[0,4,10,32],analys:[5,33],analysi:[0,5,6,11,24,26,27,29,32,33,34,35],analyt:[0,16,17,21,41],ani:[13,36,37],anoth:[9,34,35],appli:24,approach:[0,8,14,32,35,37,38],approxim:[12,39],architectur:[1,39,40,41],argument:[37,38],arithmet:42,arrai:[25,32],artifici:[38,39],assist:30,assumpt:[34,35],august:33,autocorrel:29,autograd:[2,13,21,37,38,41],automat:[13,21,37,38,41],avoid:[37,38],b:[17,26,27,37,38],back:[1,11,12,39,40,41],background:[24,26,27,35],backpropag:42,bag:10,base:[13,21,35,37,38],basic:[0,5,7,9,10,11,25,33,34,35,36],batch:[1,37,38,40,41],bay:[5,34,35],befor:11,beta:[34,35],better:[8,38,39],bia:[6,26,35],bias:[39,40,41],binari:[1,39,40],bind:32,bird:10,boldsymbol:[33,34,35],boost:10,bootstrap:[6,10,35],boston:[0,33],breast:[1,40,41],brief:[32,35,36,37,42],bring:[12,39],build:[1,3,9,23,40,41,42],c:[26,27,32],calcul:33,can:[21,32,35,37,38],cancer:[1,7,9,11,36,40,41],cart:9,center:33,central:[13,24,29,35,36,37],chain:[12,39],challeng:36,chang:10,channel:32,chi:[0,32],choic:[40,41],choos:[1,39,40,41],cifar01:[3,42],classic:11,classif:[1,9,10,23,27,36,39,40,41],classifi:[8,36],clip:[1,40,41],cluster:14,cnn:[3,42],code:[0,1,2,5,9,11,12,13,14,21,23,27,32,33,34,35,36,37,38,39,40,41,42],coeffici:42,collect:[1,3,39,40,41,42],come:36,commun:32,compact:36,compar:[2,10,41],comparison:34,compet:[21,37,38],compil:42,complet:33,complex:[0,6,26,33],complic:[6,37,38],compon:11,comput:[9,37,38],computation:35,computerlab:32,con:9,concept:29,condit:[34,35,36,37],confid:35,conjug:[13,37],connect:42,construct:[39,40,41],content:42,continu:38,contn:32,convex:[8,13,36,37],convolut:[3,12,38,39,42],convolution2dlay:42,correctli:[34,35],correl:[11,33,36,42],correspond:[36,37],cost:[1,10,23,33,34,35,36,37,39,40,41,42],cours:[24,31,32],covari:[5,11,29,33],cover:32,critic:27,cross:[6,26,35,36],ct:42,cython:32,d:[26,27],data:[0,1,3,6,7,9,11,15,16,23,24,26,29,32,33,34,36,39,40,41,42],dataset:[1,3,39,40,41,42],deadlin:[26,27,32],decai:[2,37,38,41],decis:[9,10],decomposit:[5,11,17,25,33],deep:[1,2,36,40,41],defin:[1,32,39,40,41],definit:39,degre:[0,33],deliveri:[26,27],delta:35,demonstr:42,dens:[0,32,42],deriv:[5,12,33,34,35,36,37,39,40,41],descent:[2,10,13,21,27,36,37,38,41],descript:26,design:33,detail:[3,32,41,42],develop:[1,39,40,41],diagon:11,differ:[8,27,37,38,42],different:21,differenti:[2,13,37,38,41],diffus:[2,41],dimension:[2,3,8,23,26,33,41,42],directli:[37,38],disadvantag:9,discret:29,discuss:36,distribut:[5,29,34,35],distrubut:[34,35],doe:[33,34,38,39],domain:[29,42],don:42,dot:[37,38],down:[1,40,41],dropout:[1,40,41],e:[26,27],each:36,economi:33,effici:42,electron:[26,27],element:[0,29,32,37,38],elimin:25,energi:32,ensembl:10,entropi:[9,36],environ:[0,15,32],equat:[0,2,12,32,33,34,36,37,39,41],error:[0,10,32,33,35],essenti:32,estim:[34,35],et:21,etc:[32,42],euler:[2,41],evalu:[1,27,39,40,41,42],exampl:[0,1,2,3,4,6,7,8,9,10,21,32,33,34,35,36,37,38,39,40,41,42],exercis:[0,6,15,16,17,18,19,20,21,22,23,32,33,35,41],expect:[18,29,34,35],expens:35,experi:29,explor:[0,15,16,32],exponenti:[2,41],express:[17,18,32,33,36,37],extend:[36,37],extrapol:4,extrem:[10,32],ey:10,f:[26,27],fall:30,famili:[1,32,33,40,41],famou:25,fantast:33,featur:[9,25,33],feed:[1,12,38,39,40,41],file:42,find:[35,37,38,42],fine:[1,40,41],first:[4,12,27,32,33,34,36,37,39,41],fit:[0,10,32,34],fix:33,flatten:42,flow:40,fold:[35,36],forc:[3,42],forest:10,format:[26,27,32],forward:[1,2,12,38,39,40,41],fourier:[3,42],frank:[6,26,33],freedom:[0,33],frequent:33,frequentist:[0,32],fridai:36,from:[5,10,12,21,27,33,34,35,36,37,38,39,42],full:[2,39,40,41,42],fulli:42,funtion:[40,41],further:[3,5,33,42],g:26,gan:4,gate:[23,38,39,40,41],gaussian:25,gd:[13,21,37,38],gener:[4,9,32],geometr:[11,36,37],get:21,gini:9,good:[0,32],goodfellow:21,grade: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