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  • + + Project 3 on Machine Learning, deadline December 18 (midnight), 2023 + +
    • diff --git a/doc/LectureNotes/_build/html/objects.inv b/doc/LectureNotes/_build/html/objects.inv index 2edd0fda7..e9a6fa619 100644 Binary files a/doc/LectureNotes/_build/html/objects.inv and b/doc/LectureNotes/_build/html/objects.inv differ diff --git a/doc/LectureNotes/_build/html/search.html b/doc/LectureNotes/_build/html/search.html index 4e6e035a3..794188678 100644 --- a/doc/LectureNotes/_build/html/search.html +++ b/doc/LectureNotes/_build/html/search.html @@ -377,6 +377,11 @@ const thebe_selector_output = ".output, .cell_output" Project 2 on Machine Learning, deadline November 17 (Midnight) +
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3 on Machine Learning, deadline December 18 (midnight), 2023","3. Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. 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","2. 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 17 (Midnight)","Teaching schedule with links to material","1. 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)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random Forests"],titleterms:{"1":[1,16,17,18,19,27,33,34],"12":40,"13":[],"14":36,"17":28,"18":0,"19":41,"2":[1,16,17,18,19,28,33,34,43],"2023":[0,27,31],"21":37,"23":37,"26":42,"3":[0,1,16,17,33,34],"31":34,"34":[16,33],"35":[17,34],"36":[18,35],"37":[19,36],"38":[20,37],"39":[21,22,38],"3d":43,"4":[1,34],"40":[22,39],"41":[22,40],"42":[23,41],"43":[24,42],"44":[24,42,43],"45":44,"46":45,"5":1,"7":35,"9":[27,44],"case":[9,11,30,34,35,37,38],"class":37,"do":[2,33,35,36,38,39,40,41,42,45],"final":[13,22,28,34,35,38,39,40,42,43,45],"function":[1,2,7,8,9,11,12,13,14,22,24,27,28,30,33,34,35,36,37,38,39,40,41,42,43,45],"import":[6,22,26,33,34,35,43,45],"long":44,"new":[5,35,36,44],"short":44,"try":44,A:[1,2,5,9,10,22,33,35,36,37,41,42,43,44,45],AND:[24,39,40,41,42],And:[22,33,34,35,37,38,39],But:[22,38,39],In:31,Is:[41,42],Ising:7,No:43,OR:[39,40,41,42],The:[0,1,2,3,4,6,7,8,9,10,12,13,18,24,25,33,34,35,36,37,38,39,40,41,42,43,44,45],To:[33,34],With:[5,35,44],about:[33,34],abov:[24,35,42,43],activ:[2,13,24,28,35,39,40,41,42,43,44],ad:[1,7,18,27,33,34,39,40],adaboost:[11,45],adagrad:[14,22,38,39],adam:[14,22,38,39],adapt:[11,22,38,39,45],add:43,addit:[43,44,45],adjust:[2,40,41,42],advanc:22,adversari:5,again:[4,10,37,43,44],ai:33,aim:[9,10,18,19,20,21,22,23,24,33,45],aka:[33,34],al:22,algebra:[26,33],algorithm:[10,11,12,13,22,28,34,38,39,40,41,45],algortithm:[14,37,38],all:[9,44],altern:0,an:[1,5,11,33,44,45],analys:[6,34],analysi:[1,6,7,12,25,27,28,30,33,34,35,36],analyt:[1,17,18,22,42],analyz:0,ani:[14,37,38],anoth:[10,35,36],appli:25,approach:[1,9,15,33,36,38,39,45],approxim:[13,40],architectur:[2,40,41,42],argument:[38,39],arithmet:43,arrai:[26,33],artifici:[39,40],assist:31,assumpt:[35,36],august:34,autocorrel:30,autograd:[3,14,22,38,39,42],automat:[14,22,38,39,42],avoid:[38,39],b:[0,18,27,28,38,39],back:[2,12,13,40,41,42],background:[25,27,28,36],backpropag:[43,44],backward:44,bag:[11,45],base:[14,22,36,38,39],basic:[0,1,6,8,10,11,12,26,34,35,36,37,44,45],batch:[2,38,39,41,42],bay:[6,35,36],befor:12,beta:[35,36],better:[9,39,40],bia:[7,27,36],bias:[40,41,42],binari:[2,40,41,45],bind:33,bird:[11,45],boldsymbol:[34,35,36],boost:[11,45],bootstrap:[7,11,36,45],boston:[1,34],breast:[2,41,42],brief:[33,36,37,38,43],bring:[13,40],build:[2,4,10,24,41,42,43,45],c:[0,27,28,33],calcul:34,can:[22,33,36,38,39,44],cancer:[2,8,10,12,37,41,42,44,45],cart:[10,45],cell:44,center:34,central:[14,25,30,36,37,38],chain:[13,40],challeng:37,chang:[11,45],channel:33,chi:[1,33],choic:[41,42],choos:[2,40,41,42],cifar01:[4,43],classic:12,classif:[2,10,11,24,28,37,40,41,42,44,45],classifi:[9,37,45],clip:[2,41,42],cluster:15,cnn:[4,43],code:[1,2,3,6,10,12,13,14,15,22,24,28,33,34,35,36,37,38,39,40,41,42,43,45],coeffici:43,coin:45,collect:[2,4,40,41,42,43],combin:44,come:37,commun:33,compact:37,compar:[3,11,42,45],comparison:35,compet:[22,38,39],compil:43,complet:34,complex:[0,1,7,27,34,45],complic:[7,38,39],compon:12,comput:[10,38,39,45],computation:36,computerlab:33,con:[10,45],concept:30,condit:[35,36,37,38],confid:36,conjug:[14,38],connect:43,construct:[40,41,42],content:43,continu:39,contn:33,convex:[9,14,37,38],convolut:[4,13,39,40,43],convolution2dlay:43,correctli:[35,36],correl:[12,34,37,43,44],correspond:[37,38],cost:[2,11,24,34,35,36,37,38,40,41,42,43,45],cours:[25,32,33],covari:[6,12,30,34],cover:33,critic:28,cross:[7,27,36,37],ct:43,cumul:44,curv:44,cython:33,d:[0,27,28],data:[0,1,2,4,7,8,10,12,16,17,24,25,27,30,33,34,35,37,40,41,42,43,44,45],dataset:[2,4,40,41,42,43],deadlin:[0,27,28,33],decai:[3,38,39,42],decemb:0,decis:[10,11,45],decomposit:[6,12,18,26,34],deep:[2,3,37,41,42],defin:[0,2,33,40,41,42],definit:40,degre:[1,34],deliveri:[0,27,28],delta:36,demonstr:43,dens:[1,33,43],deriv:[6,13,34,35,36,37,38,40,41,42],descent:[3,11,14,22,28,37,38,39,42],descript:27,design:34,detail:[4,33,42,43],develop:[2,40,41,42],diagon:12,differ:[9,28,38,39,43],different:22,differenti:[0,3,14,38,39,42],diffus:[3,42],dimension:[3,4,9,24,27,34,42,43],directli:[38,39],disadvantag:[10,45],discret:30,discuss:[37,44],distribut:[6,30,35,36],distrubut:[35,36],doe:[34,35,39,40],domain:[30,43],don:43,dot:[38,39],down:[2,41,42,45],dropout:[2,41,42],e:[0,27,28],each:37,economi:34,effect:44,effici:43,electron:[0,27,28],element:[1,30,33,38,39],elimin:26,energi:33,ensembl:[11,45],entropi:[10,37],environ:[1,16,33],equat:[0,1,3,13,33,34,35,37,38,40,42],error:[1,11,33,34,36,45],essenti:33,estim:[35,36],et:22,etc:[33,43],euler:[3,42],evalu:[2,28,40,41,42,43],exampl:[1,2,3,4,5,7,8,9,10,11,22,33,34,35,36,37,38,39,40,41,42,43,44,45],exercis:[1,7,16,17,18,19,20,21,22,23,24,33,34,36,42],expect:[19,30,35,36],expens:36,experi:30,explod:44,explor:[1,16,17,33],exponenti:[3,42],express:[18,19,33,34,37,38],extend:[37,38],extrapol:[5,44],extrem:[11,33,45],ey:[11,45],f:[27,28],f_1:44,factor:45,fall:31,famili:[2,33,34,41,42],famou:26,fantast:34,featur:[10,26,34,45],feed:[2,13,39,40,41,42],file:43,find:[36,38,39,43],fine:[2,41,42],first:[5,13,28,33,34,35,37,38,40,42,45],fit:[1,11,33,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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. 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","2. 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 17 (Midnight)","Project 3 on Machine Learning, deadline December 18 (midnight), 2023","Teaching schedule with links to material","1. 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)","Week 45, Recurrent Neural Networks","Week 46: Decision Trees, Ensemble methods and Random 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