From 5c267c6f2487b380f39d1ece07cc44c3ac0de440 Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Mon, 18 Sep 2023 07:37:20 +0200
Subject: [PATCH] adding files

---
 .../_build/.doctrees/environment.pickle       | Bin 338825 -> 338899 bytes
 .../_build/.doctrees/intro.doctree            | Bin 52992 -> 53009 bytes
 .../_build/.doctrees/week38.doctree           | Bin 1191541 -> 1191541 bytes
 doc/LectureNotes/_build/html/genindex.html    |   5 +++
 doc/LectureNotes/_build/html/intro.html       |   5 +++
 doc/LectureNotes/_build/html/search.html      |   5 +++
 doc/LectureNotes/_build/html/searchindex.js   |   2 +-
 doc/LectureNotes/_build/html/week38.html      |  32 ++++++++++++++++--
 .../_build/jupyter_execute/week38.ipynb       |   2 +-
 9 files changed, 46 insertions(+), 5 deletions(-)

diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index a73e1c7ef4239b41bfe6272d27d18e9dba76f583..51825a5f4e781661b2fded9b9379143b9e4086fe 100644
GIT binary patch
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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","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","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: Statitsitcal interpretations and Resampling Methods","Data Analysis and Machine Learning: Logistic Regression and 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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","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","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: Statitsitcal interpretations and Resampling Methods","Data Analysis and Machine Learning: Logistic Regression and Optimization"],titleterms:{"1":[0,15,16,17,18,22,27,28],"10":[],"11":[],"12":[],"13":[],"14":30,"15":[],"16":[],"17":[],"18":[],"19":[],"2":[0,15,16,17,18,27,28],"20":[],"2021":[],"2022":[],"2023":[22,25],"21":31,"22":[],"23":31,"24":[],"25":[],"26":[],"27":[],"28":[],"29":[],"3":[0,15,16,27,28],"30":[],"31":28,"34":[15,27],"35":[16,28],"36":[17,29],"37":[18,30],"38":[19,31],"39":[],"4":[0,28],"40":[],"41":[],"4155":[],"42":[],"43":[],"44":[],"45":[],"46":[],"47":[],"5":0,"6":[],"7":29,"8":[],"9":22,"case":[8,10,24,28,29,31],"class":31,"do":[1,27,29,30],"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],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],actual:[],ad:[0,6,17,22,27,28],adaboost:10,adagrad:13,adam:13,adapt:10,adjust:1,adversari:4,again:[3,9,31],ai: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:[0,5,6,11,20,22,24,27,28,29,30,31],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,binomi:[],bird:10,block:[],boldsymbol:[28,29,30],boost:10,bootstrap:[6,10,30],boston:[0,28],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,continu:[],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],cumul:[],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:[1,2,31],defin:[1,27],definit:[],degre:[0,28],deliveri:22,delta:30,demonstr:[],dens:[0,27],deriv:[5,12,28,29,30,31],descent:[2,10,13,31],descript:22,design:28,detail:[3,27],develop:1,deviat:[],diagon:11,dice:[],differ:8,differenti:[2,13],diffus:2,dimension:[2,3,8,22,28],disadvantag:9,discret:24,discuss:31,disguis:[],distribut:[5,24,29,30],distrubut:[29,30],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,event:[],exampl:[0,1,2,3,4,6,7,8,9,10,27,28,29,30,31],exercis:[0,6,15,16,17,18,19,27,28,30],expect:[18,24,29,30],expens:30,experi:24,explor:[0,15,16,27],exponenti:2,express:[17,18,27,28,31],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:31,from:[5,10,12,28,29,30,31],full:2,further:[3,5,28],fy:[],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],have:27,heard:27,hessian:[28,31],hidden:2,histogram:30,homework:31,hous:[0,28],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,invok:[],iter:10,its:28,jackknif:[],jacobian:28,jax:13,julia:27,jungl:10,k:[30,31],kera:[1,3],kernel:[8,11],lagrangian:8,lambda:31,lasso:[5,6,22,28,29,30,31],last:[28,30,31],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],mai:27,main:24,make:[0,9,10,15,16,27,28],mani:[10,12],manipul:28,margin:[29,30],mass:27,materi:[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:[0,27,28,29,31],measur:31,meet:[5,10,24,27,28],mercer:8,mersenn:[],method:[6,9,10,13,22,30,31],midnight:22,min:28,minim:[27,31],ml:27,mle:[29,30],mlp:12,mnist:[3,4],model:[0,1,4,6,12,27],moment:[],momentum:13,moon:[8,9],more:[3,6,21,22,27,28,29,30,31],multilay:12,multipl:[1,3],multipli:8,name:[],need:[22,27],network:[1,2,3,4,7,12,27,31],neural:[1,2,3,4,7,12,27],newton:31,non:8,normal:[0,1,30],norwai:[],notat:12,note:[28,29,30],novemb:[],now:[1,9,13,29,30],nuclear:[0,27],nueral:31,numba:27,number:[0,2,24,28],numer:[2,22,24],numpi:[21,27],object:3,observ:[],obtain:11,octob:22,od:2,off:[6,22],ol:[5,6,22,29,30],one:[2,12,31],oper:21,optim:[1,8,13,20,27,28,31],order:13,ordinari:[5,6,18,22,27,28,29],organ:[0,27],oslo:26,other:[4,9,11,12,21,27,28,31],our:[0,4,5,11,13,27,28,31],outcom:[20,27],output:2,overarch:[0,4,8,9,17,18,19,27,28],overview:[10,27],own:[0,10,11,15,16,27,28],packag:[21,27],panda:[27,28],paper:22,paramet:[27,28,31],part:[13,20,22,28,31],partial:2,pass:1,pca:11,pdf:24,pencil:22,perceptron:12,perform:[1,9],period:3,perspect:1,plan:[28,29,30,31],plot:[30,31],point:4,poisson:2,polynomi:[3,29],popul:2,practic:[13,25,27],pre:[1,3],predict:4,predictor:31,preprocess:28,prerequisit:[3,20,27],princip:11,principl:3,pro:9,probabl:[5,24,29,30],problem:[1,2,13,27,28,29,31],procedur:[9,27],process:[1,3],program:[2,13,22,31],project:[6,22,27,31],prop:13,propag:[1,12],properti:[5,24,28,31],pseudo:[],python:[0,9,15,20,21,27],quick:8,r:27,ran0:[],random:[10,11,24,31],raphson:31,read:[9,27,28,30],real:[6,22,27],recip:[],recommend:[27,28],rectangular:29,recurr:[4,12],reduc:[0,28],reduct:3,reformul:2,regress:[0,5,6,7,9,10,13,17,18,22,27,28,29,30,31],regular:[1,31],relat:28,relev:[26,28,30,31],relu:1,remark:3,remind:[6,8,27,31],repeat:28,replac:13,report:22,repositori:30,requir:[2,20],resampl:[6,22,29,30],rescal:[6,29],residu:28,resourc:2,result:[28,29],revisit:[13,31],rewrit:[27,28,30],rewritten:31,ridg:[0,5,6,17,18,22,28,29,30,31],rm:13,rng:[],rule:12,s:[8,10,30,31],same:[13,30],sampl:11,scale:[28,30],schedul:[23,27],schemat:9,scheme:2,scienc:27,scikit:[0,1,11,15,16,27,28,29,30,31],search:31,second:13,select:31,semest:25,sensit:31,septemb:[29,30,31],session:29,set:[0,2,3,9,12,15,27,28,31],sgd:13,should:1,similar:13,simpl:[0,4,9,13,27,28,29,31],singl:10,singular:[5,11,17,28],situat:[],size:28,soft:8,softmax:1,softwar:[22,27],solv:[2,29,31],solver:13,some:[13,21,27,28,31],specifi:2,split:[0,15,16,27,28],squar:[0,5,6,10,18,22,27,28,29],standard:[13,28,30],state:[0,27],statement:27,statist:[5,6,20,24,27,29,30],statitsitc:30,steepest:[10,13,31],step:[30,31],still:28,stk3155:[],stochast:[13,24],strongli:27,studi:31,subtract:28,suggest:27,sum:30,summari:[25,27],superposit:3,supervis:1,support:8,svd:[5,28,29],systemat:3,t:[28,29],teach:[23,25],teacher:[25,27],technic:29,techniqu:[6,11,22,29],technolog:20,tensorflow:[1,3],tent:27,term:30,test:[0,1,15,16,27,28,29],textbook:[26,27],than:31,thei:27,theorem:[5,8,11,12,24,29,30],theori:24,thi:[17,18,19,27],think:28,three:[],thursdai:[28,29,30,31],tip:13,togeth:12,tool:27,top:1,topic:27,toss:[],toward:11,trade:[6,22],tradeoff:[6,30],train:[0,1,4,15,16,27,28],transform:3,tree:[9,10],tuesdai:29,tune:1,two:[3,8,20,22,28,31],type:[2,4,12,27],uio:27,uncorrel:[],understand:30,uniform:[],univers:[12,26],unsupervis:14,up:[0,2,9,12,15,27,28,30,31],us:[0,1,2,3,7,13,20,27,28,31],usag:[29,30],valid:[6,22,30,31],valu:[5,11,17,18,24,28,29,30,31],variabl:[24,31],varianc:[6,22,29,30],variou:[0,15,27,30,31],vector:[8,12,21,27,28],view:[0,4,10,28],visual:[1,9],vs:3,wai:[9,30],wave:2,we:27,wednesdai:29,week:[15,16,17,18,19,27,28,29,30,31],weekend:31,weekli:[23,30],what:[0,27,28,29,30],which:1,why:[27,28,30],wisconsin:[7,31],wrap:[28,30],write:[4,11,29],x:[28,29],xgboost:10,yet:29,you:27,your:[0,10,15,16,27,28],yourself:31}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html
index 36a19191c..eb89f3616 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -55,6 +55,8 @@ const thebe_selector_output = ".output, .cell_output"
     <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Project 1 on Machine Learning, deadline October 9 (midnight), 2023" href="project1.html" />
+    <link rel="prev" title="Exercises week 38" href="exercisesweek38.html" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="docsearch:language" content="None">
     
@@ -245,7 +247,7 @@ const thebe_selector_output = ".output, .cell_output"
   Weekly material, notes and exercises
  </span>
 </p>
-<ul class="nav bd-sidenav">
+<ul class="current nav bd-sidenav">
  <li class="toctree-l1">
   <a class="reference internal" href="exercisesweek34.html">
    Exercises week 34
@@ -286,6 +288,16 @@ const thebe_selector_output = ".output, .cell_output"
    Week 37: Statitsitcal interpretations and Resampling Methods
   </a>
  </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek38.html">
+   Exercises week 38
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Data Analysis and Machine Learning: Logistic Regression and Optimization
+  </a>
+ </li>
 </ul>
 <p aria-level="2" class="caption" role="heading">
  <span class="caption-text">
@@ -1982,7 +1994,7 @@ case under study.</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x11e02eaf0&gt;})
+                   param_distributions={&#39;alpha&#39;: &lt;scipy.stats._distn_infrastructure.rv_frozen object at 0x130057e80&gt;})
 Best estimated lambda-value: 0.9849967686928113
 MSE score: 1.0853136633465326
 R2 score: -0.0002382102844775691
@@ -2880,7 +2892,21 @@ minimum of this function.</p>
               
             
                 <!-- Previous / next buttons -->
-<div class='prev-next-area'>
+<div class='prev-next-area'> 
+    <a class='left-prev' id="prev-link" href="exercisesweek38.html" title="previous page">
+        <i class="fas fa-angle-left"></i>
+        <div class="prev-next-info">
+            <p class="prev-next-subtitle">previous</p>
+            <p class="prev-next-title">Exercises week 38</p>
+        </div>
+    </a>
+    <a class='right-next' id="next-link" href="project1.html" title="next page">
+    <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</p>
+    </div>
+    <i class="fas fa-angle-right"></i>
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diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
index 0adebec78..bcbb9aa2b 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb
@@ -1679,7 +1679,7 @@
      "output_type": "stream",
      "text": [
       "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
-      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11e02eaf0>})\n",
+      "                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x130057e80>})\n",
       "Best estimated lambda-value: 0.9849967686928113\n",
       "MSE score: 1.0853136633465326\n",
       "R2 score: -0.0002382102844775691\n"