udpate

doc/LectureNotes/_build/html/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: d4110f82058cef301334e6f3b0981f88
+config: c810468e5ce15e612839c964bc8fee3e
 tags: 645f666f9bcd5a90fca523b33c5a78b7

doc/LectureNotes/_build/html/_static/basic.css

@@ -222,7 +222,7 @@ table.modindextable td {
 /* -- general body styles --------------------------------------------------- */
 
 div.body {
-    min-width: 360px;
+    min-width: 450px;
     max-width: 800px;
 }
 
@@ -237,6 +237,16 @@ a.headerlink {
     visibility: hidden;
 }
 
+a.brackets:before,
+span.brackets > a:before{
+    content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+    content: "]";
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -324,16 +334,12 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
-nav.contents,
-aside.topic,
 
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
-nav.contents,
-aside.topic,
 
 div.topic {
     border: 1px solid #ccc;
@@ -373,9 +379,6 @@ div.body p.centered {
 
 div.sidebar > :last-child,
 aside.sidebar > :last-child,
-nav.contents > :last-child,
-aside.topic > :last-child,
-
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -383,9 +386,6 @@ div.admonition > :last-child {
 
 div.sidebar::after,
 aside.sidebar::after,
-nav.contents::after,
-aside.topic::after,
-
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -428,6 +428,10 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
 th {
     text-align: left;
     padding-right: 5px;
@@ -611,7 +615,6 @@ ul.simple p {
     margin-bottom: 0;
 }
 
-/* Docutils 0.17 and older (footnotes & citations) */
 dl.footnote > dt,
 dl.citation > dt {
     float: left;
@@ -629,33 +632,6 @@ dl.citation > dd:after {
     clear: both;
 }
 
-/* Docutils 0.18+ (footnotes & citations) */
-aside.footnote > span,
-div.citation > span {
-    float: left;
-}
-aside.footnote > span:last-of-type,
-div.citation > span:last-of-type {
-  padding-right: 0.5em;
-}
-aside.footnote > p {
-  margin-left: 2em;
-}
-div.citation > p {
-  margin-left: 4em;
-}
-aside.footnote > p:last-of-type,
-div.citation > p:last-of-type {
-    margin-bottom: 0em;
-}
-aside.footnote > p:last-of-type:after,
-div.citation > p:last-of-type:after {
-    content: "";
-    clear: both;
-}
-
-/* Footnotes & citations ends */
-
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;

doc/LectureNotes/_build/html/_static/copybutton.css

@@ -35,8 +35,7 @@ div.highlight  {
     position: relative;
 }
 
-/* Show the copybutton */
-.highlight:hover button.copybtn, button.copybtn.success {
+.highlight:hover button.copybtn {
 	opacity: 1;
 }
 

doc/LectureNotes/_build/html/_static/copybutton.js

@@ -20,7 +20,7 @@ const messages = {
   },
   'fr' : {
     'copy': 'Copier',
-    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_to_clipboard': 'Copié dans le presse-papier',
     'copy_success': 'Copié !',
     'copy_failure': 'Échec de la copie',
   },
@@ -102,25 +102,18 @@ const clearSelection = () => {
   }
 }
 
-// Changes tooltip text for a moment, then changes it back
-// We want the timeout of our `success` class to be a bit shorter than the
-// tooltip and icon change, so that we can hide the icon before changing back.
-var timeoutIcon = 2000;
-var timeoutSuccessClass = 1500;
-
+// Changes tooltip text for two seconds, then changes it back
 const temporarilyChangeTooltip = (el, oldText, newText) => {
   el.setAttribute('data-tooltip', newText)
   el.classList.add('success')
-  // Remove success a little bit sooner than we change the tooltip
-  // So that we can use CSS to hide the copybutton first
-  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
+  setTimeout(() => el.classList.remove('success'), 2000)
 }
 
 // Changes the copy button icon for two seconds, then changes it back
 const temporarilyChangeIcon = (el) => {
   el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+  setTimeout(() => {el.innerHTML = iconCopy}, 2000)
 }
 
 const addCopyButtonToCodeCells = () => {
@@ -132,8 +125,7 @@ const addCopyButtonToCodeCells = () => {
   }
 
   // Add copybuttons to all of our code cells
-  const COPYBUTTON_SELECTOR = 'div.highlight pre';
-  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  const codeCells = document.querySelectorAll('div.highlight pre')
   codeCells.forEach((codeCell, index) => {
     const id = codeCellId(index)
     codeCell.setAttribute('id', id)
@@ -149,25 +141,10 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+
     var regexp;
     var match;
 
@@ -222,12 +199,7 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl
 
 var copyTargetText = (trigger) => {
   var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-
-  // get filtered text
-  let exclude = '.linenos';
-
-  let text = filterText(target, exclude);
-  return formatCopyText(text, '', false, true, true, true, '', '')
+  return formatCopyText(target.innerText, '', false, true, true, true, '', '')
 }
 
   // Initialize with a callback so we can modify the text before copy

doc/LectureNotes/_build/html/_static/copybutton_funcs.js

@@ -2,25 +2,10 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-export function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+
     var regexp;
     var match;
 

doc/LectureNotes/_build/html/_static/doctools.js

@@ -2,263 +2,325 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Base JavaScript utilities for all Sphinx HTML documentation.
+ * Sphinx JavaScript utilities for all documentation.
  *
  * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+  if (!x) {
+    return x
   }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
 };
 
 /**
- * highlight a given string on a node by wrapping it in
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
  * span elements with the given class name.
  */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
           document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
       }
     }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
   }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
 };
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
 
 /**
  * Small JavaScript module for the documentation.
  */
-const Documentation = {
-  init: () => {
-    Documentation.highlightSearchWords();
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
+      this.initOnKeyListeners();
+    }
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
   },
 
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
   },
 
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
   },
 
   /**
    * highlight the search words provided in the url in the text
    */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
 
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
   },
 
   /**
    * helper function to hide the search marks again
    */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
   },
 
   /**
-   * helper function to focus on search bar
+   * make the url absolute
    */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
   },
 
   /**
-   * Initialise the domain index toggle buttons
+   * get the current relative url
    */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
   },
 
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
-
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
-    document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
-
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
+  initOnKeyListeners: function() {
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box, textarea, dropdown or button
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
+        switch (event.keyCode) {
+          case 37: // left
+            var prevHref = $('link[rel="prev"]').prop('href');
+            if (prevHref) {
+              window.location.href = prevHref;
+              return false;
             }
             break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
+          case 39: // right
+            var nextHref = $('link[rel="next"]').prop('href');
+            if (nextHref) {
+              window.location.href = nextHref;
+              return false;
             }
             break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
     });
-  },
+  }
 };
 
 // quick alias for translations
-const _ = Documentation.gettext;
+_ = Documentation.gettext;
 
-_ready(Documentation.init);
+$(document).ready(function() {
+  Documentation.init();
+});

doc/LectureNotes/_build/html/_static/documentation_options.js

@@ -1,14 +1,12 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
     VERSION: '',
-    LANGUAGE: 'en',
+    LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
     LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '',
-    NAVIGATION_WITH_KEYS: true,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    NAVIGATION_WITH_KEYS: true
 };

doc/LectureNotes/_build/html/_static/language_data.js

@@ -10,7 +10,7 @@
  *
  */
 
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
 /* Non-minified version is copied as a separate JS file, is available */
@@ -197,3 +197,101 @@ var Stemmer = function() {
   }
 }
 
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+

doc/LectureNotes/_build/html/_static/pygments.css

@@ -1,152 +1,75 @@
-html[data-theme="light"] .highlight pre { line-height: 125%; }
-html[data-theme="light"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-html[data-theme="light"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-html[data-theme="light"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-html[data-theme="light"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-html[data-theme="light"] .highlight .hll { background-color: #7971292e }
-html[data-theme="light"] .highlight { background: #fefefe; color: #545454 }
-html[data-theme="light"] .highlight .c { color: #797129 } /* Comment */
-html[data-theme="light"] .highlight .err { color: #d91e18 } /* Error */
-html[data-theme="light"] .highlight .k { color: #7928a1 } /* Keyword */
-html[data-theme="light"] .highlight .l { color: #797129 } /* Literal */
-html[data-theme="light"] .highlight .n { color: #545454 } /* Name */
-html[data-theme="light"] .highlight .o { color: #008000 } /* Operator */
-html[data-theme="light"] .highlight .p { color: #545454 } /* Punctuation */
-html[data-theme="light"] .highlight .ch { color: #797129 } /* Comment.Hashbang */
-html[data-theme="light"] .highlight .cm { color: #797129 } /* Comment.Multiline */
-html[data-theme="light"] .highlight .cp { color: #797129 } /* Comment.Preproc */
-html[data-theme="light"] .highlight .cpf { color: #797129 } /* Comment.PreprocFile */
-html[data-theme="light"] .highlight .c1 { color: #797129 } /* Comment.Single */
-html[data-theme="light"] .highlight .cs { color: #797129 } /* Comment.Special */
-html[data-theme="light"] .highlight .gd { color: #007faa } /* Generic.Deleted */
-html[data-theme="light"] .highlight .ge { font-style: italic } /* Generic.Emph */
-html[data-theme="light"] .highlight .gh { color: #007faa } /* Generic.Heading */
-html[data-theme="light"] .highlight .gs { font-weight: bold } /* Generic.Strong */
-html[data-theme="light"] .highlight .gu { color: #007faa } /* Generic.Subheading */
-html[data-theme="light"] .highlight .kc { color: #7928a1 } /* Keyword.Constant */
-html[data-theme="light"] .highlight .kd { color: #7928a1 } /* Keyword.Declaration */
-html[data-theme="light"] .highlight .kn { color: #7928a1 } /* Keyword.Namespace */
-html[data-theme="light"] .highlight .kp { color: #7928a1 } /* Keyword.Pseudo */
-html[data-theme="light"] .highlight .kr { color: #7928a1 } /* Keyword.Reserved */
-html[data-theme="light"] .highlight .kt { color: #797129 } /* Keyword.Type */
-html[data-theme="light"] .highlight .ld { color: #797129 } /* Literal.Date */
-html[data-theme="light"] .highlight .m { color: #797129 } /* Literal.Number */
-html[data-theme="light"] .highlight .s { color: #008000 } /* Literal.String */
-html[data-theme="light"] .highlight .na { color: #797129 } /* Name.Attribute */
-html[data-theme="light"] .highlight .nb { color: #797129 } /* Name.Builtin */
-html[data-theme="light"] .highlight .nc { color: #007faa } /* Name.Class */
-html[data-theme="light"] .highlight .no { color: #007faa } /* Name.Constant */
-html[data-theme="light"] .highlight .nd { color: #797129 } /* Name.Decorator */
-html[data-theme="light"] .highlight .ni { color: #008000 } /* Name.Entity */
-html[data-theme="light"] .highlight .ne { color: #7928a1 } /* Name.Exception */
-html[data-theme="light"] .highlight .nf { color: #007faa } /* Name.Function */
-html[data-theme="light"] .highlight .nl { color: #797129 } /* Name.Label */
-html[data-theme="light"] .highlight .nn { color: #545454 } /* Name.Namespace */
-html[data-theme="light"] .highlight .nx { color: #545454 } /* Name.Other */
-html[data-theme="light"] .highlight .py { color: #007faa } /* Name.Property */
-html[data-theme="light"] .highlight .nt { color: #007faa } /* Name.Tag */
-html[data-theme="light"] .highlight .nv { color: #d91e18 } /* Name.Variable */
-html[data-theme="light"] .highlight .ow { color: #7928a1 } /* Operator.Word */
-html[data-theme="light"] .highlight .pm { color: #545454 } /* Punctuation.Marker */
-html[data-theme="light"] .highlight .w { color: #545454 } /* Text.Whitespace */
-html[data-theme="light"] .highlight .mb { color: #797129 } /* Literal.Number.Bin */
-html[data-theme="light"] .highlight .mf { color: #797129 } /* Literal.Number.Float */
-html[data-theme="light"] .highlight .mh { color: #797129 } /* Literal.Number.Hex */
-html[data-theme="light"] .highlight .mi { color: #797129 } /* Literal.Number.Integer */
-html[data-theme="light"] .highlight .mo { color: #797129 } /* Literal.Number.Oct */
-html[data-theme="light"] .highlight .sa { color: #008000 } /* Literal.String.Affix */
-html[data-theme="light"] .highlight .sb { color: #008000 } /* Literal.String.Backtick */
-html[data-theme="light"] .highlight .sc { color: #008000 } /* Literal.String.Char */
-html[data-theme="light"] .highlight .dl { color: #008000 } /* Literal.String.Delimiter */
-html[data-theme="light"] .highlight .sd { color: #008000 } /* Literal.String.Doc */
-html[data-theme="light"] .highlight .s2 { color: #008000 } /* Literal.String.Double */
-html[data-theme="light"] .highlight .se { color: #008000 } /* Literal.String.Escape */
-html[data-theme="light"] .highlight .sh { color: #008000 } /* Literal.String.Heredoc */
-html[data-theme="light"] .highlight .si { color: #008000 } /* Literal.String.Interpol */
-html[data-theme="light"] .highlight .sx { color: #008000 } /* Literal.String.Other */
-html[data-theme="light"] .highlight .sr { color: #d91e18 } /* Literal.String.Regex */
-html[data-theme="light"] .highlight .s1 { color: #008000 } /* Literal.String.Single */
-html[data-theme="light"] .highlight .ss { color: #007faa } /* Literal.String.Symbol */
-html[data-theme="light"] .highlight .bp { color: #797129 } /* Name.Builtin.Pseudo */
-html[data-theme="light"] .highlight .fm { color: #007faa } /* Name.Function.Magic */
-html[data-theme="light"] .highlight .vc { color: #d91e18 } /* Name.Variable.Class */
-html[data-theme="light"] .highlight .vg { color: #d91e18 } /* Name.Variable.Global */
-html[data-theme="light"] .highlight .vi { color: #d91e18 } /* Name.Variable.Instance */
-html[data-theme="light"] .highlight .vm { color: #797129 } /* Name.Variable.Magic */
-html[data-theme="light"] .highlight .il { color: #797129 } /* Literal.Number.Integer.Long */
-html[data-theme="dark"] .highlight pre { line-height: 125%; }
-html[data-theme="dark"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-html[data-theme="dark"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-html[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-html[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-html[data-theme="dark"] .highlight .hll { background-color: #ffd9002e }
-html[data-theme="dark"] .highlight { background: #2b2b2b; color: #f8f8f2 }
-html[data-theme="dark"] .highlight .c { color: #ffd900 } /* Comment */
-html[data-theme="dark"] .highlight .err { color: #ffa07a } /* Error */
-html[data-theme="dark"] .highlight .k { color: #dcc6e0 } /* Keyword */
-html[data-theme="dark"] .highlight .l { color: #ffd900 } /* Literal */
-html[data-theme="dark"] .highlight .n { color: #f8f8f2 } /* Name */
-html[data-theme="dark"] .highlight .o { color: #abe338 } /* Operator */
-html[data-theme="dark"] .highlight .p { color: #f8f8f2 } /* Punctuation */
-html[data-theme="dark"] .highlight .ch { color: #ffd900 } /* Comment.Hashbang */
-html[data-theme="dark"] .highlight .cm { color: #ffd900 } /* Comment.Multiline */
-html[data-theme="dark"] .highlight .cp { color: #ffd900 } /* Comment.Preproc */
-html[data-theme="dark"] .highlight .cpf { color: #ffd900 } /* Comment.PreprocFile */
-html[data-theme="dark"] .highlight .c1 { color: #ffd900 } /* Comment.Single */
-html[data-theme="dark"] .highlight .cs { color: #ffd900 } /* Comment.Special */
-html[data-theme="dark"] .highlight .gd { color: #00e0e0 } /* Generic.Deleted */
-html[data-theme="dark"] .highlight .ge { font-style: italic } /* Generic.Emph */
-html[data-theme="dark"] .highlight .gh { color: #00e0e0 } /* Generic.Heading */
-html[data-theme="dark"] .highlight .gs { font-weight: bold } /* Generic.Strong */
-html[data-theme="dark"] .highlight .gu { color: #00e0e0 } /* Generic.Subheading */
-html[data-theme="dark"] .highlight .kc { color: #dcc6e0 } /* Keyword.Constant */
-html[data-theme="dark"] .highlight .kd { color: #dcc6e0 } /* Keyword.Declaration */
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-html[data-theme="dark"] .highlight .kr { color: #dcc6e0 } /* Keyword.Reserved */
-html[data-theme="dark"] .highlight .kt { color: #ffd900 } /* Keyword.Type */
-html[data-theme="dark"] .highlight .ld { color: #ffd900 } /* Literal.Date */
-html[data-theme="dark"] .highlight .m { color: #ffd900 } /* Literal.Number */
-html[data-theme="dark"] .highlight .s { color: #abe338 } /* Literal.String */
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-html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
-html[data-theme="dark"] .highlight .no { color: #00e0e0 } /* Name.Constant */
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-html[data-theme="dark"] .highlight .ne { color: #dcc6e0 } /* Name.Exception */
-html[data-theme="dark"] .highlight .nf { color: #00e0e0 } /* Name.Function */
-html[data-theme="dark"] .highlight .nl { color: #ffd900 } /* Name.Label */
-html[data-theme="dark"] .highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-html[data-theme="dark"] .highlight .nx { color: #f8f8f2 } /* Name.Other */
-html[data-theme="dark"] .highlight .py { color: #00e0e0 } /* Name.Property */
-html[data-theme="dark"] .highlight .nt { color: #00e0e0 } /* Name.Tag */
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-html[data-theme="dark"] .highlight .ow { color: #dcc6e0 } /* Operator.Word */
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-html[data-theme="dark"] .highlight .mf { color: #ffd900 } /* Literal.Number.Float */
-html[data-theme="dark"] .highlight .mh { color: #ffd900 } /* Literal.Number.Hex */
-html[data-theme="dark"] .highlight .mi { color: #ffd900 } /* Literal.Number.Integer */
-html[data-theme="dark"] .highlight .mo { color: #ffd900 } /* Literal.Number.Oct */
-html[data-theme="dark"] .highlight .sa { color: #abe338 } /* Literal.String.Affix */
-html[data-theme="dark"] .highlight .sb { color: #abe338 } /* Literal.String.Backtick */
-html[data-theme="dark"] .highlight .sc { color: #abe338 } /* Literal.String.Char */
-html[data-theme="dark"] .highlight .dl { color: #abe338 } /* Literal.String.Delimiter */
-html[data-theme="dark"] .highlight .sd { color: #abe338 } /* Literal.String.Doc */
-html[data-theme="dark"] .highlight .s2 { color: #abe338 } /* Literal.String.Double */
-html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
-html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
-html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
-html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
-html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
-html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
-html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
-html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
-html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
-html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
-html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
-html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
-html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
-html[data-theme="dark"] .highlight .il { color: #ffd900 } /* Literal.Number.Integer.Long */
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
+.highlight .mh { color: #208050 } /* Literal.Number.Hex */
+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
+.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
+.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
+.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
+.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */

doc/LectureNotes/_build/html/_static/searchtools.js

@@ -8,20 +8,18 @@
  * :license: BSD, see LICENSE for details.
  *
  */
-"use strict";
 
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
   var Scorer = {
     // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // The function takes a result array [filename, title, anchor, descr, score]
     // and returns the new score.
     /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
+    score: function(result) {
+      return result[4];
     },
     */
 
@@ -30,11 +28,9 @@ if (typeof Scorer === "undefined") {
     // or matches in the last dotted part of the object name
     objPartialMatch: 6,
     // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
     //  Used when the priority is not in the mapping.
     objPrioDefault: 0,
 
@@ -43,455 +39,456 @@ if (typeof Scorer === "undefined") {
     partialTitle: 7,
     // query found in terms
     term: 5,
-    partialTerm: 2,
+    partialTerm: 2
   };
 }
 
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, highlightTerms, searchTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-
-  const [docName, title, anchor, descr] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
   }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
-  linkEl.innerHTML = title;
-  if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
-      " (" + descr + ")";
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
-          );
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  highlightTerms,
-  searchTerms
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
 }
 
 /**
  * Search Module
  */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
+var Search = {
 
-  htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
-    );
-    return "";
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
   },
 
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
   },
 
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
 
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
     }
   },
 
-  hasIndex: () => Search._index !== null,
+  hasIndex : function() {
+      return this._index !== null;
+  },
 
-  deferQuery: (query) => (Search._queued_query = query),
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
 
-  stopPulse: () => (Search._pulse_status = -1),
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
 
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch: (query) => {
+  performSearch : function(query) {
     // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
 
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
 
     // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
   },
 
   /**
    * execute search (requires search index to be loaded)
    */
-  query: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
+  query : function(query) {
+    var i;
 
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
       }
-    });
 
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      // prevent stemmer from cutting word smaller than two chars
+      if(word.length < 3 && tmp[i].length >= 3) {
+        word = tmp[i];
+      }
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
-    _removeChildren(document.getElementById("search-progress"));
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
 
     // lookup as object
-    objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
-    );
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
 
     // now sort the results by score (in opposite order of appearance, since the
     // display function below uses pop() to retrieve items) and then
     // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
         // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
       }
-      return leftScore > rightScore ? 1 : -1;
     });
 
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    results = results.reverse();
-
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
+    //console.info('search results:', Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
+                    }
+                    Search.output.append(listItem);
+                    setTimeout(function() {
+                      displayNextItem();
+                    }, 5);
+                  }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
   },
 
   /**
    * search for object names
    */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
 
-    const results = [];
+    var i;
+    var results = [];
 
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
+    for (var prefix in objects) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
 
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
       }
+    }
 
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
     return results;
   },
 
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
-    const filenames = Search._index.filenames;
-    const titles = Search._index.titles;
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
 
-    const scoreMap = new Map();
-    const fileMap = new Map();
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
 
     // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
       ];
       // add support for partial matches
       if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        var word_regex = this.escapeRegExp(word);
+        for (var w in terms) {
+          if (w.match(word_regex) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word_regex) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
       }
 
       // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
       // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
 
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
 
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
       });
 
       // create the mapping
-      files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
-      });
-    });
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
 
     // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
+    for (file in fileMap) {
+      var valid = true;
 
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
       if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
 
       // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
 
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
     }
     return results;
   },
@@ -499,33 +496,34 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
+   * of stemmed words, hlwords is the list of normal, unstemmed
    * words. the first one is used to find the occurrence, the
    * latter for highlighting it.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
-    if (text === "") return null;
-
-    const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("div");
-    summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
-
-    return summary;
-  },
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<p class="context"></p>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
 };
 
-_ready(Search.init);
+$(document).ready(function() {
+  Search.init();
+});

doc/LectureNotes/_build/html/_static/webpack-macros.html

@@ -1,31 +1,25 @@
-<!--
-  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
-  These are re-used in layout.html
--->
-{# Load FontAwesome icons #}
+<!-- these macros are generated by "yarn build:production". do not edit by hand. -->
 {% macro head_pre_icons() %}
-  <link href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=ac02cc09edc035673794" rel="stylesheet" />
-  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" />
-<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" />
-<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" />
+  <link rel="stylesheet"
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/css/all.min.css', 1) }}">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2', 1) }}">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="{{ pathto('_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2', 1) }}">
 {% endmacro %}
 
-{% macro head_pre_assets() %}
-  <!-- Loaded before other Sphinx assets -->
-  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=ac02cc09edc035673794" rel="stylesheet" />
-<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=ac02cc09edc035673794" rel="stylesheet" />
-<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=ac02cc09edc035673794" rel="stylesheet" />
+{% macro head_pre_fonts() %}
+{% endmacro %}
+
+{% macro head_pre_bootstrap() %}
+  <link href="{{ pathto('_static/css/theme.css', 1) }}" rel="stylesheet">
+  <link href="{{ pathto('_static/css/index.ff1ffe594081f20da1ef19478df9384b.css', 1) }}" rel="stylesheet">
 {% endmacro %}
 
 {% macro head_js_preload() %}
-  <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=ac02cc09edc035673794" />
-<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=ac02cc09edc035673794" />
-  <script src="{{ pathto('_static/vendor/fontawesome/6.1.2/js/all.min.js', 1) }}?digest=ac02cc09edc035673794"></script>
+  <link rel="preload" as="script" href="{{ pathto('_static/js/index.be7d3bbb2ef33a8344ce.js', 1) }}">
 {% endmacro %}
 
 {% macro body_post() %}
-  <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=ac02cc09edc035673794"></script>
-<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=ac02cc09edc035673794"></script>
+  <script src="{{ pathto('_static/js/index.be7d3bbb2ef33a8344ce.js', 1) }}"></script>
 {% endmacro %}

doc/LectureNotes/_build/html/exercisesweek35.html

@@ -1,61 +1,50 @@
 
-
 <!DOCTYPE html>
 
-
-<html lang="en" >
-
+<html>
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Exercises week 35 &#8212; Applied Data Analysis and Machine Learning</title>
-  
-  
-  
-  <script data-cfasync="false">
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
-    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
-  </script>
-  
-  <!-- Loaded before other Sphinx assets -->
-  <link href="_static/styles/theme.css?digest=ac02cc09edc035673794" rel="stylesheet" />
-<link href="_static/styles/bootstrap.css?digest=ac02cc09edc035673794" rel="stylesheet" />
-<link href="_static/styles/pydata-sphinx-theme.css?digest=ac02cc09edc035673794" rel="stylesheet" />
+    
+  <link href="_static/css/theme.css" rel="stylesheet">
+  <link href="_static/css/index.ff1ffe594081f20da1ef19478df9384b.css" rel="stylesheet">
 
-  
-  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=ac02cc09edc035673794" rel="stylesheet" />
-  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" />
-<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" />
-<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
+    
+  <link rel="stylesheet"
+    href="_static/vendor/fontawesome/5.13.0/css/all.min.css">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.woff2">
+  <link rel="preload" as="font" type="font/woff2" crossorigin
+    href="_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
 
+    
+      
+
+    
     <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-    <link rel="stylesheet" href="_static/styles/sphinx-book-theme.css?digest=14f4ca6b54d191a8c7657f6c759bf11a5fb86285" type="text/css" />
+    <link rel="stylesheet" type="text/css" href="_static/sphinx-book-theme.css?digest=c3fdc42140077d1ad13ad2f1588a4309" />
     <link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
     <link rel="stylesheet" type="text/css" href="_static/copybutton.css" />
-    <link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
+    <link rel="stylesheet" type="text/css" href="_static/mystnb.css" />
     <link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css" />
-    <link rel="stylesheet" type="text/css" href="_static/design-style.4045f2051d55cab465a707391d5b2007.min.css" />
-  
-  <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=ac02cc09edc035673794" />
-<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=ac02cc09edc035673794" />
-  <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=ac02cc09edc035673794"></script>
+    <link rel="stylesheet" type="text/css" href="_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
+    <link rel="stylesheet" type="text/css" href="_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
+    
+  <link rel="preload" as="script" href="_static/js/index.be7d3bbb2ef33a8344ce.js">
 
     <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
     <script src="_static/jquery.js"></script>
     <script src="_static/underscore.js"></script>
-    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="_static/doctools.js"></script>
     <script src="_static/clipboard.min.js"></script>
     <script src="_static/copybutton.js"></script>
-    <script src="_static/scripts/sphinx-book-theme.js?digest=5a5c038af52cf7bc1a1ec88eea08e6366ee68824"></script>
     <script>let toggleHintShow = 'Click to show';</script>
     <script>let toggleHintHide = 'Click to hide';</script>
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-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
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+</p>
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-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
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+ <li class="toctree-l1">
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+ </li>
+ <li class="toctree-l1">
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+   Textbooks
+  </a>
+ </li>
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-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
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-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
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-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
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-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
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+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   17. Recurrent neural networks: Overarching view
+  </a>
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+  Weekly material, notes and exercises
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-<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
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+   Exercises week 39
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+ </li>
+ <li class="toctree-l1">
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+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
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+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-making-your-own-data-and-exploring-scikit-learn">Exercise 2: making your own data and exploring scikit-learn</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-split-data-in-test-and-training-data">Exercise 3: Split data in test and training data</a></li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-1-analytical-exercises">
+   Exercise 1: Analytical exercises
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+  <a class="reference internal nav-link" href="#exercise-2-making-your-own-data-and-exploring-scikit-learn">
+   Exercise 2: making your own data and exploring scikit-learn
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+  <a class="reference internal nav-link" href="#exercise-3-split-data-in-test-and-training-data">
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+                <h1>Exercises week 35</h1>
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+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-2-making-your-own-data-and-exploring-scikit-learn">
+   Exercise 2: making your own data and exploring scikit-learn
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-3-split-data-in-test-and-training-data">
+   Exercise 3: Split data in test and training data
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   <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
 doconce format html exercisesweek35.do.txt  -->
-<!-- dom:TITLE: Exercises week 35 --><section class="tex2jax_ignore mathjax_ignore" id="exercises-week-35">
-<h1>Exercises week 35<a class="headerlink" href="#exercises-week-35" title="Permalink to this heading">#</a></h1>
+<!-- dom:TITLE: Exercises week 35 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-35">
+<h1>Exercises week 35<a class="headerlink" href="#exercises-week-35" title="Permalink to this headline">¶</a></h1>
 <p><strong>August 28-September 1, 2023</strong></p>
 <p>Date: <strong>Deadline is Friday September 1 at midnight</strong></p>
-<section id="exercise-1-analytical-exercises">
-<h2>Exercise 1: Analytical exercises<a class="headerlink" href="#exercise-1-analytical-exercises" title="Permalink to this heading">#</a></h2>
+<div class="section" id="exercise-1-analytical-exercises">
+<h2>Exercise 1: Analytical exercises<a class="headerlink" href="#exercise-1-analytical-exercises" title="Permalink to this headline">¶</a></h2>
 <p>In this exercise we derive the expressions for various derivatives of
 products of vectors and matrices. Such derivatives are central to the
 optimization of various cost functions. Although we will often use
@@ -470,9 +510,9 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
 \[
 \frac{\partial f(\boldsymbol{x})}{\partial \boldsymbol{x}}= \boldsymbol{A}.
 \]</div>
-</section>
-<section id="exercise-2-making-your-own-data-and-exploring-scikit-learn">
-<h2>Exercise 2: making your own data and exploring scikit-learn<a class="headerlink" href="#exercise-2-making-your-own-data-and-exploring-scikit-learn" title="Permalink to this heading">#</a></h2>
+</div>
+<div class="section" id="exercise-2-making-your-own-data-and-exploring-scikit-learn">
+<h2>Exercise 2: making your own data and exploring scikit-learn<a class="headerlink" href="#exercise-2-making-your-own-data-and-exploring-scikit-learn" title="Permalink to this headline">¶</a></h2>
 <p>We will generate our own dataset for a function <span class="math notranslate nohighlight">\(y(x)\)</span> where <span class="math notranslate nohighlight">\(x \in
 [0,1]\)</span> and defined by random numbers computed with the uniform
 distribution. The function <span class="math notranslate nohighlight">\(y\)</span> is a quadratic polynomial in <span class="math notranslate nohighlight">\(x\)</span> with
@@ -488,7 +528,7 @@ and <span class="math notranslate nohighlight">\(y\)</span> values (with 100 dat
 </div>
 </div>
 </div>
-<ol class="arabic simple">
+<ol class="simple">
 <li><p>Write your own code (following the examples under the <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html">regression notes</a>) for computing the parametrization of the data set fitting a second-order polynomial.</p></li>
 <li><p>Use thereafter <strong>scikit-learn</strong> (see again the examples in the slides for week 35) and compare with your own code. Note here that <strong>scikit-learn</strong> does not include, by default, the intercept. See the discussions on scaling your data in the slides for this week. This type of problems appear in particular if we fit a polynomial with an intercept.</p></li>
 <li><p>Using scikit-learn, compute also the mean squared error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as</p></li>
@@ -511,9 +551,9 @@ R^2(\boldsymbol{y}, \tilde{\boldsymbol{y}}) = 1 - \frac{\sum_{i=0}^{n - 1} (y_i
 \]</div>
 <p>You can use the functionality included in scikit-learn. If you feel for it, you can use your own program and define functions which compute the above two functions.
 Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits.</p>
-</section>
-<section id="exercise-3-split-data-in-test-and-training-data">
-<h2>Exercise 3: Split data in test and training data<a class="headerlink" href="#exercise-3-split-data-in-test-and-training-data" title="Permalink to this heading">#</a></h2>
+</div>
+<div class="section" id="exercise-3-split-data-in-test-and-training-data">
+<h2>Exercise 3: Split data in test and training data<a class="headerlink" href="#exercise-3-split-data-in-test-and-training-data" title="Permalink to this headline">¶</a></h2>
 <p>In this exercise we want you to to compute the MSE for the training
 data and the test data as function of the complexity of a polynomial,
 that is the degree of a given polynomial.</p>
@@ -539,8 +579,8 @@ Write thereafter (using either <strong>scikit-learn</strong> or your matrix inve
 and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.  If you compare your own code with <em>scikit_learn</em>, not that the latter does not include by default the intercept. See the discussions on scaling your data in the slides for this week.</p>
 <p><strong>c)</strong>
 Add now a model which allows you to make polynomials up to degree <span class="math notranslate nohighlight">\(15\)</span>.  Perform a standard OLS fitting of the training data and compute the MSE for the training and test data and plot both test and training data MSE as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?</p>
-</section>
-</section>
+</div>
+</div>
 
     <script type="text/x-thebe-config">
     {
@@ -554,7 +594,7 @@ Add now a model which allows you to make polynomials up to degree <span class="m
             mode: "python"
         },
         kernelOptions: {
-            name: "python3",
+            kernelName: "python3",
             path: "./."
         },
         predefinedOutput: true
@@ -562,103 +602,44 @@ Add now a model which allows you to make polynomials up to degree <span class="m
     </script>
     <script>kernelName = 'python3'</script>
 
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+   Applied Data Analysis and Machine Learning
+  </a>
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-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="schedule.html">
+   Teaching schedule with links to material
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="teachers.html">
+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercises week 37</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="exercisesweek38.html">Exercises week 38</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Projects
+ </span>
+</p>
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-<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="project1.html">
+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
+  </a>
+ </li>
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-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-expectation-values-for-ordinary-least-squares-expressions">Exercise 1: Expectation values for ordinary least squares expressions</a></li>
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-1-expectation-values-for-ordinary-least-squares-expressions">
+   Exercise 1: Expectation values for ordinary least squares expressions
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-2-expectation-values-for-ridge-regression">
+   Exercise 2: Expectation values for Ridge regression
+  </a>
+ </li>
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+
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+                <h1>Exercises week 37</h1>
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#overarching-aims-of-the-exercises-this-week">
+   Overarching aims of the exercises this week
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-1-expectation-values-for-ordinary-least-squares-expressions">
+   Exercise 1: Expectation values for ordinary least squares expressions
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#exercise-2-expectation-values-for-ridge-regression">
+   Exercise 2: Expectation values for Ridge regression
+  </a>
+ </li>
+</ul>
 
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   <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
 doconce format html exercisesweek37.do.txt  -->
-<!-- dom:TITLE: Exercises week 37 --><section class="tex2jax_ignore mathjax_ignore" id="exercises-week-37">
-<h1>Exercises week 37<a class="headerlink" href="#exercises-week-37" title="Permalink to this heading">#</a></h1>
+<!-- dom:TITLE: Exercises week 37 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-37">
+<h1>Exercises week 37<a class="headerlink" href="#exercises-week-37" title="Permalink to this headline">¶</a></h1>
 <p><strong>September 11-15, 2023</strong></p>
 <p>Date: <strong>Deadline is Sunday September 17 at midnight</strong></p>
-<section id="overarching-aims-of-the-exercises-this-week">
-<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this heading">#</a></h2>
+<div class="section" id="overarching-aims-of-the-exercises-this-week">
+<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h2>
 <p>This exercise deals with various mean values and variances in linear
 regression method (here it may be useful to look up chapter 3,
 equation (3.8) of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome
@@ -448,9 +488,9 @@ function <span class="math notranslate nohighlight">\(f\)</span> is approximated
 \boldsymbol{\tilde{y}} = \boldsymbol{X}\boldsymbol{\beta}.
 \]</div>
 <p>The matrix <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> is the so-called design or feature matrix.</p>
-</section>
-<section id="exercise-1-expectation-values-for-ordinary-least-squares-expressions">
-<h2>Exercise 1: Expectation values for ordinary least squares expressions<a class="headerlink" href="#exercise-1-expectation-values-for-ordinary-least-squares-expressions" title="Permalink to this heading">#</a></h2>
+</div>
+<div class="section" id="exercise-1-expectation-values-for-ordinary-least-squares-expressions">
+<h2>Exercise 1: Expectation values for ordinary least squares expressions<a class="headerlink" href="#exercise-1-expectation-values-for-ordinary-least-squares-expressions" title="Permalink to this headline">¶</a></h2>
 <p>Show that  the expectation value of <span class="math notranslate nohighlight">\(\boldsymbol{y}\)</span> for a given element <span class="math notranslate nohighlight">\(i\)</span></p>
 <div class="math notranslate nohighlight">
 \[
@@ -476,9 +516,9 @@ mean value <span class="math notranslate nohighlight">\(\boldsymbol{X}\boldsymbo
 \]</div>
 <p>We can use the last expression when we define a <a class="reference external" href="https://en.wikipedia.org/wiki/Confidence_interval">so-called confidence interval</a> for the parameters <span class="math notranslate nohighlight">\(\beta\)</span>.
 A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span> is given by the diagonal matrix element of the above matrix.</p>
-</section>
-<section id="exercise-2-expectation-values-for-ridge-regression">
-<h2>Exercise 2: Expectation values for Ridge regression<a class="headerlink" href="#exercise-2-expectation-values-for-ridge-regression" title="Permalink to this heading">#</a></h2>
+</div>
+<div class="section" id="exercise-2-expectation-values-for-ridge-regression">
+<h2>Exercise 2: Expectation values for Ridge regression<a class="headerlink" href="#exercise-2-expectation-values-for-ridge-regression" title="Permalink to this headline">¶</a></h2>
 <p>Show that</p>
 <div class="math notranslate nohighlight">
 \[
@@ -492,8 +532,8 @@ A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span>
 \mbox{Var}[\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}]=\sigma^2[  \mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I} ]^{-1}  \mathbf{X}^{T}\mathbf{X} \{ [  \mathbf{X}^{\top} \mathbf{X} + \lambda \mathbf{I} ]^{-1}\}^{T},
 \]</div>
 <p>and it is easy to see that if the parameter <span class="math notranslate nohighlight">\(\lambda\)</span> goes to infinity then the variance of the Ridge parameters <span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span> goes to zero.</p>
-</section>
-</section>
+</div>
+</div>
 
     <script type="text/x-thebe-config">
     {
@@ -507,7 +547,7 @@ A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span>
             mode: "python"
         },
         kernelOptions: {
-            name: "python3",
+            kernelName: "python3",
             path: "./."
         },
         predefinedOutput: true
@@ -515,103 +555,44 @@ A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span>
     </script>
     <script>kernelName = 'python3'</script>
 
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+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
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-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical interpretations and Resampling Methods</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercises week 38</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical interpretations and Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Exercises week 38
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
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-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Projects
+ </span>
+</p>
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-<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</a></li>
+ <li class="toctree-l1">
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+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
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+ </li>
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   <!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)
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-<!-- dom:TITLE: Exercises week 38 --><section class="tex2jax_ignore mathjax_ignore" id="exercises-week-38">
-<h1>Exercises week 38<a class="headerlink" href="#exercises-week-38" title="Permalink to this heading">#</a></h1>
+<!-- dom:TITLE: Exercises week 38 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-38">
+<h1>Exercises week 38<a class="headerlink" href="#exercises-week-38" title="Permalink to this headline">¶</a></h1>
 <p><strong>September 18-22, 2023</strong></p>
 <p>Date: <strong>Deadline is Sunday September 24 at midnight</strong></p>
-<section id="overarching-aims-of-the-exercises-this-week">
-<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this heading">#</a></h2>
+<div class="section" id="overarching-aims-of-the-exercises-this-week">
+<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h2>
 <p>The aim of the exercises this week is to derive the equations for the bias-variance tradeoff to be used in project 1 as well as testing this for a simpler function using the bootstrap method. The exercises here can be reused in project 1 as well.</p>
 <p>Consider a
 dataset <span class="math notranslate nohighlight">\(\mathcal{L}\)</span> consisting of the data
@@ -467,9 +489,9 @@ studying the MSE value as function of the complexity of your model. Use ordinary
 of your model complexity (the degree of the polynomial) and the number
 of data points, and possibly also your training and test data using the <strong>bootstrap</strong> resampling method.
 You can follow the code example in the jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#the-bias-variance-tradeoff</a>.</p>
-<p>See also the whiteboard notes from week 37 at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf">CompPhysics/MachineLearning</a></p>
-</section>
-</section>
+<p>See also the whiteboard notes from week 37 at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf">https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep14.pdf</a></p>
+</div>
+</div>
 
     <script type="text/x-thebe-config">
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@@ -483,7 +505,7 @@ You can follow the code example in the jupyter-book at <a class="reference exter
             mode: "python"
         },
         kernelOptions: {
-            name: "python3",
+            kernelName: "python3",
             path: "./."
         },
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@@ -491,101 +513,44 @@ You can follow the code example in the jupyter-book at <a class="reference exter
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+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="schedule.html">
+   Teaching schedule with links to material
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+ </li>
+ <li class="toctree-l1">
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+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical 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"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Optimization and  Gradient Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
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-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
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+ <span class="caption-text">
+  Projects
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+</p>
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+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
+  </a>
+ </li>
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-<!-- dom:TITLE: Exercises week 39 --><section class="tex2jax_ignore mathjax_ignore" id="exercises-week-39">
-<h1>Exercises week 39<a class="headerlink" href="#exercises-week-39" title="Permalink to this heading">#</a></h1>
+<!-- dom:TITLE: Exercises week 39 --><div class="tex2jax_ignore mathjax_ignore section" id="exercises-week-39">
+<h1>Exercises week 39<a class="headerlink" href="#exercises-week-39" title="Permalink to this headline">¶</a></h1>
 <p><strong>September 25-29, 2023</strong></p>
 <p>Date: <strong>Deadline is Sunday October 1 at midnight</strong></p>
-<section id="overarching-aims-of-the-exercises-this-week">
-<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this heading">#</a></h2>
+<div class="section" id="overarching-aims-of-the-exercises-this-week">
+<h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h2>
 <p>The aim of the exercises this week is to aid you in getting started
 with writing the report. This will be discussed during the lab
 sessions as well. One of the lab sessions will be recorded.</p>
-<p>A general guideline can be found at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md">CompPhysics/MachineLearning</a>.</p>
-<p>Similarly, an example of an earlier project can be found at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf">CompPhysics/MachineLearning</a></p>
+<p>A general guideline can be found at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md">https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/EvaluationGrading/EvaluationForm.md</a>.</p>
+<p>Similarly, an example of an earlier project can be found at <a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf">https://github.com/CompPhysics/MachineLearning/blob/master/doc/Projects/ReportExample/ReportSample.pdf</a></p>
 <p>Your task this week is to</p>
-<ol class="arabic simple">
+<ol class="simple">
 <li><p>Write an abstract for your project</p></li>
 <li><p>Write an introduction</p></li>
 <li><p>Include references</p></li>
 </ol>
 <p>Ashort feedback to the this exercise will be available after the deadline. And you can reuse these elements in your final report.</p>
-</section>
-</section>
+</div>
+</div>
 
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+            kernelName: "python3",
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@@ -456,101 +476,44 @@ sessions as well. One of the lab sessions will be recorded.</p>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="intro.html">
+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="schedule.html">
+   Teaching schedule with links to material
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="teachers.html">
+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical 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"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek39.html">Exercises week 39</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Optimization and  Gradient Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Projects
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="project1.html">
+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
+  </a>
+ </li>
 </ul>
 
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+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="current nav bd-sidenav">
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Teaching schedule with links to material</a></li>
-<li class="toctree-l1"><a class="reference internal" href="teachers.html">Teachers and Grading</a></li>
-<li class="toctree-l1"><a class="reference internal" href="textbooks.html">Textbooks</a></li>
-
+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Teaching schedule with links to material
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="teachers.html">
+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical 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"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
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+   Exercises week 37
+  </a>
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+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
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+   Exercises week 39
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+   Week 39: Optimization and  Gradient Methods
+  </a>
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+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
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+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#weekly-schedule">
+   Weekly Schedule
+  </a>
+ </li>
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+
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+   Weekly Schedule
+  </a>
+ </li>
+</ul>
 
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-<h1>Teaching schedule with links to material<a class="headerlink" href="#teaching-schedule-with-links-to-material" title="Permalink to this heading">#</a></h1>
+  <div class="tex2jax_ignore mathjax_ignore section" id="teaching-schedule-with-links-to-material">
+<h1>Teaching schedule with links to material<a class="headerlink" href="#teaching-schedule-with-links-to-material" title="Permalink to this headline">¶</a></h1>
 <p>This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.</p>
-<ol class="arabic simple">
+<ol class="simple">
 <li><p>Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;</p></li>
 <li><p>Two hours of laboratory sessions for work on computational projects and exercises for each group. There will  also be fully digital laboratory sessions for those who cannot attend;</p></li>
 <li><p>Three projects which are graded and count 1/3 each of the final grade. The deadlines for the projects are October 7 for project 1, November 11 for project 2 and December 9 for project 3.</p></li>
@@ -393,8 +446,8 @@ document.write(`
 <li><p>Videos of teaching material are available via the links at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>;</p></li>
 <li><p>Weekly emails with summary of activities will be mailed to all participants;</p></li>
 </ol>
-<section id="weekly-schedule">
-<h2>Weekly Schedule<a class="headerlink" href="#weekly-schedule" title="Permalink to this heading">#</a></h2>
+<div class="section" id="weekly-schedule">
+<h2>Weekly Schedule<a class="headerlink" href="#weekly-schedule" title="Permalink to this headline">¶</a></h2>
 <p>For the reading assignments we use the following abbreviations:</p>
 <ul class="simple">
 <li><p>GBC: Goodfellow, Bengio, and Courville, Deep Learning</p></li>
@@ -402,8 +455,8 @@ document.write(`
 <li><p>HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning</p></li>
 <li><p>AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow</p></li>
 </ul>
-</section>
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+</div>
+</div>
 
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+ <li class="toctree-l1">
+  <a class="reference internal" href="intro.html">
+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="schedule.html">
+   Teaching schedule with links to material
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="teachers.html">
+   Teachers and Grading
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="textbooks.html">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical 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"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek39.html">Exercises week 39</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week39.html">Week 39: Optimization and  Gradient Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 9 (midnight), 2023</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Projects
+ </span>
+</p>
+<ul class="nav bd-sidenav">
+ <li class="toctree-l1">
+  <a class="reference internal" href="project1.html">
+   Project 1 on Machine Learning, deadline October 9 (midnight), 2023
+  </a>
+ </li>
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+      
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+  <a class="reference internal" href="intro.html">
+   Applied Data Analysis and Machine Learning
+  </a>
+ </li>
+</ul>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  About the course
+ </span>
+</p>
 <ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="schedule.html">Teaching schedule with links to material</a></li>
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+   Teaching schedule with links to material
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+   Teachers and Grading
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+ <li class="toctree-l1 current active">
+  <a class="current reference internal" href="#">
+   Textbooks
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Review of Statistics with Resampling Techniques and Linear Algebra
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
-<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="statistics.html">
+   1. Elements of Probability Theory and Statistical Data Analysis
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="linalg.html">
+   2. Linear Algebra, Handling of Arrays and more Python Features
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  From Regression to Support Vector Machines
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter1.html">
+   3. Linear Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter2.html">
+   4. Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter3.html">
+   5. Resampling Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter4.html">
+   6. Logistic Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapteroptimization.html">
+   7. Optimization, the central part of any Machine Learning algortithm
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter5.html">
+   8. Support Vector Machines, overarching aims
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Decision Trees, Ensemble Methods and Boosting
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter6.html">
+   9. Decision trees, overarching aims
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter7.html">
+   10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Dimensionality Reduction
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter8.html">
+   11. Basic ideas of the Principal Component Analysis (PCA)
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="clustering.html">
+   12. Clustering and Unsupervised Learning
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Deep Learning Methods
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations  with Deep Learning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
-<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
-
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter9.html">
+   13. Neural networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter10.html">
+   14. Building a Feed Forward Neural Network
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter11.html">
+   15. Solving Differential Equations  with Deep Learning
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter12.html">
+   16. Convolutional Neural Networks
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="chapter13.html">
+   17. Recurrent neural networks: Overarching view
+  </a>
+ </li>
 </ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
+<p aria-level="2" class="caption" role="heading">
+ <span class="caption-text">
+  Weekly material, notes and exercises
+ </span>
+</p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek34.html">Exercises week 34</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Statistical interpretation of Linear Regression and Resampling techniques</a></li>
-<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
-<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Statistical 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"><a class="reference internal" href="week38.html">Week 38: Logistic Regression and Optimization</a></li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek34.html">
+   Exercises week 34
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week34.html">
+   Week 34: Introduction to the course, Logistics and Practicalities
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek35.html">
+   Exercises week 35
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week35.html">
+   Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek36.html">
+   Exercises week 36
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week36.html">
+   Week 36: Statistical interpretation of Linear Regression and Resampling techniques
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek37.html">
+   Exercises week 37
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week37.html">
+   Week 37: Statistical 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">
+  <a class="reference internal" href="week38.html">
+   Week 38: Logistic Regression and Optimization
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="exercisesweek39.html">
+   Exercises week 39
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week39.html">
+   Week 39: Optimization and  Gradient Methods
+  </a>
+ </li>
+ <li class="toctree-l1">
+  <a class="reference internal" href="week40.html">
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  </a>
+ </li>
 </ul>
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+<p aria-level="2" class="caption" role="heading">
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-  <section id="textbooks">
-<h1>Textbooks<a class="headerlink" href="#textbooks" title="Permalink to this heading">#</a></h1>
+  <div class="section" id="textbooks">
+<h1>Textbooks<a class="headerlink" href="#textbooks" title="Permalink to this headline">¶</a></h1>
 <p><em>Recommended textbooks</em>:
 The lecture notes are collected as a jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</a>. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.</p>
 <ul class="simple">
@@ -408,9 +469,9 @@ The lecture notes are collected as a jupyter-book at <a class="reference externa
 <li><p>David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press</p></li>
 <li><p>David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press</p></li>
 </ul>
-</section>
-<section id="links-to-relevant-courses-at-the-university-of-oslo">
-<h1>Links to relevant courses at the University of Oslo<a class="headerlink" href="#links-to-relevant-courses-at-the-university-of-oslo" title="Permalink to this heading">#</a></h1>
+</div>
+<div class="section" id="links-to-relevant-courses-at-the-university-of-oslo">
+<h1>Links to relevant courses at the University of Oslo<a class="headerlink" href="#links-to-relevant-courses-at-the-university-of-oslo" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
 <li><p><em>FYS5429 Advanced Machine Learning for the Physical Sciences</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html">https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html</a></p></li>
 <li><p><em>FYS5419 Quantum Computing and Quantum Machine Learning</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html">https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html</a></p></li>
@@ -424,7 +485,7 @@ The lecture notes are collected as a jupyter-book at <a class="reference externa
 <li><p><em>STK4051 Computational Statistics</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html">https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html</a></p></li>
 <li><p><em>STK4021 Applied Bayesian Analysis and Numerical Methods</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK4021/">https://www.uio.no/studier/emner/matnat/math/STK4021/</a></p></li>
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@@ -438,7 +499,7 @@ The lecture notes are collected as a jupyter-book at <a class="reference externa
             mode: "python"
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-            name: "python3",
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@@ -446,103 +507,44 @@ The lecture notes are collected as a jupyter-book at <a class="reference externa
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