diff --git a/01-python-grundlagen/folien-code/folien-code.ipynb b/01-python-grundlagen/folien-code/folien-code.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7f99f6b97436c41df72a0f2073d1b38bffda96f7
--- /dev/null
+++ b/01-python-grundlagen/folien-code/folien-code.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Code zu Folien\n",
+    "\n",
+    "\n",
+    "\n",
+    " Dieses Skript bzw. Jupyter-Notebook enthält den Code, der auch auf den Folien \"Python Grundlagen\" enthalten ist. Zum Vorbereiten, Mitmachen oder Nacharbeiten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 10                 # Datentypen werden implizit bestimmt\n",
+    "print(type(a))         # type() gibt den Typ aus\n",
+    "\n",
+    "A = 20 / 2             # Groß- / Kleinschreibung wird unterschieden\n",
+    "print(type(A))\n",
+    "\n",
+    "print(a ** 2, 7 // 2)  # ∗∗ ist der Potenzoperator, // steht für floor Division\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(A == 10)                      # Vergleichsoperatoren: <, <=, >, >=, ==, !=\n",
+    "print(A == 10 and a == 10)          # Verknüpfungsoperatoren: not, and, or\n",
+    "print((A == 10) + 1)                # implizite Konvertierung: False → 0, True → 1\n",
+    "print(isinstance(A, (int, float)))  # ist A vom Typ int oder float? wichtig um Überladung nachbilden zu können\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 'Hello'\n",
+    "b = \"world\"\n",
+    "print(a, b)  # print gibt mehrere Argumente mit Leerzeichen getrennt aus\n",
+    "\n",
+    "c = \"\"\"Hello,\n",
+    "you are my \"world\"!\"\"\"\n",
+    "print(c)\n",
+    "c = 'Hello,\\nyou are my \"world\"!'\n",
+    "print(c)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 'Hello,' + ' world'  # Leerzeichen zwischen Komma und world im String\n",
+    "print(a)\n",
+    "print(a + str(123))\n",
+    "print(len('vier'))\n",
+    "\n",
+    "print(a.replace('ello', 'i'))\n",
+    "print(a.lower())\n",
+    "print(a.endswith('.png'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 'Beispiel'\n",
+    "print(b[7], b[2], b[6], b[3])\n",
+    "\n",
+    "f = 'logo.pdf'\n",
+    "print(f[-3] + f[-2] + f[-1])\n",
+    "\n",
+    "# f[0] = 'L'  # TypeError\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = 'logo.pdf'\n",
+    "print(f[0:4])\n",
+    "print(f[:-4], f[-3:])\n",
+    "print(f[0:6:2])\n",
+    "print(f[::2])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = '../images/logo.pdf'\n",
+    "file_pos = path.rfind('/') + 1\n",
+    "print(file_pos)\n",
+    "\n",
+    "dir = path[:file_pos]\n",
+    "file = path[file_pos:]\n",
+    "print('dir:', dir, '   file:', file)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(8 + 4)\n",
+    "print(8, 4)\n",
+    "print(8); print(4)\n",
+    "print(8, 4, 2, sep=' > ', end=' (richtige Aussage)\\n\\n')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Hi {}!'.format(5))                        # formatierte(r) String / Ausgabe\n",
+    "print('{0} {1} {0}!'.format('First', 'Things'))  # {n} referenziert Parameter n\n",
+    "print('named: {test}!'.format(test=42))          # {varname} definiert Namen\n",
+    "print('significant digits: {:.2}'.format(20/3))  # {...:.2} 2 signifikante Stellen\n",
+    "print('Fill: {:04}'.format(3))                   # {...:0s} reserviert s Zeichen und füllt mit 0\n",
+    "print('Fixed point: {:.2f}'.format(3))           # {...:f} gibt Zahl als fixed point aus\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "print(f'Explicit: {3:.2f}')            # f'\\{var:...\\}' ersetzt '{:...}'.format(var)\n",
+    "print('old style: %d %.2f' % (2, 3))   # formatierte(r) String / Ausgabe\n",
+    "print('bad style: %d %d' % (2.3, 3.8)) # Typspezifizierer könnten falsch sein! ⚡\n",
+    "\n",
+    "ip = '127.0.0.1'\n",
+    "port = 8888\n",
+    "# port = '8888'  # ergibt Fehler beim alten Formatierungsstil.\n",
+    "server = ip + ':%d' % port                 # Wurde port vielleicht als String gegeben? ⚡\n",
+    "print(server)\n",
+    "server = f'{ip}:{port}'                    # Besser! Funktioniert mit strings und ints 👍\n",
+    "print(server)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if A > 10:\n",
+    "    print('too big')\n",
+    "elif A > 5:             # es kann mehrere elif-Zweige geben\n",
+    "    print('correct')\n",
+    "else:\n",
+    "    print('too small')\n",
+    "print()\n",
+    "\n",
+    "if 's' in 'Hellas':\n",
+    "    print('It is')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in 'bla':\n",
+    "    print('Buchstabe:', c)\n",
+    "print()\n",
+    "\n",
+    "for i in range(3):\n",
+    "    print('It:', i)\n",
+    "print()\n",
+    "\n",
+    "for i, c in enumerate('bla'):\n",
+    "    print('It:', i, 'Buchstabe:', c)\n",
+    "print()\n",
+    "\n",
+    "for c, s in zip('ABC', 'abc'):\n",
+    "    print(c + s)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in 'AKIS':\n",
+    "    if c == 'I':\n",
+    "        break\n",
+    "    print(c)\n",
+    "print()\n",
+    "\n",
+    "for c in 'AKIS':\n",
+    "    if c == 'I':\n",
+    "        continue\n",
+    "    print(c)\n",
+    "print()\n",
+    "\n",
+    "while True:\n",
+    "    prompt = input('> ')\n",
+    "    if prompt == 'exit':\n",
+    "        break\n",
+    "    print(prompt)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def greet(name):\n",
+    "    print('Hello', name)\n",
+    "\n",
+    "greet('and goodbye!')    # Ausgabe: Hello and goodbye!\n",
+    "\n",
+    "def greet(name='my sunshine!'):\n",
+    "    print('Hello', name)\n",
+    "\n",
+    "greet()                  # Ausgabe: Hello my sunshine!\n",
+    "greet('you')             # Ausgabe: Hello you\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f(x, y):\n",
+    "    return x**2 - y**2\n",
+    "\n",
+    "print(f(1, 2))\n",
+    "print(f(y=2, x=1))  # Reihenfolge ist egal\n",
+    "\n",
+    "def f(x=0, y=0):\n",
+    "    return x**2 - y**2\n",
+    "\n",
+    "print(f(y=2))       # x=0, y=2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def pq_formel(p, q):\n",
+    "    x1 = -p / 2 + math.sqrt(p**2 / 4 - q)\n",
+    "    x2 = -p / 2 - math.sqrt(p**2 / 4 - q)\n",
+    "    return x1, x2\n",
+    "\n",
+    "lsg1, lsg2 = pq_formel(2, 0)  # Ausgabe:\n",
+    "print('Lösungen:')            # Lösungen:\n",
+    "print('x1 =', lsg1)           # x1 = 0.0\n",
+    "print('x2 =', lsg2)           # x2 = -2.0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "liste = [1, 'text', 1/3]          # Listenelemente können verschiede Typen haben\n",
+    "liste[0] = 2                      # Listen sind veränderbar\n",
+    "liste.append(42)                  # Listen sind erweiterbar\n",
+    "print(liste)\n",
+    "\n",
+    "liste = ['tag', 'monat', 'jahr']\n",
+    "print(liste.index('monat'))       # Stelle bzw. Index des Elements 'monat'\n",
+    "\n",
+    "liste = [1, 2, 3] * 2             # Ergibt: liste = [1, 2, 3, 1, 2, 3]\n",
+    "print(liste)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = (3, 'text')               # Tupel-Elemente können auch verschiedene Typen haben\n",
+    "print(t[1])                   # Lesen geht\n",
+    "# t[1] = 5                    # Schreiben nicht (TypeError)\n",
+    "\n",
+    "print((3, 4) + (6, 8))        # Ergibt: (3, 4, 6, 8)\n",
+    "\n",
+    "c, d = 5, 7                   # c ist 5 und d ist 7 (``unpacking''), 5, 7 ist hier ein Tupel\n",
+    "print(c, d)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = {11, 7, 3, 13, 2, 6}         # #*set* wird definiert mit #*\\{...\\}*\n",
+    "print(s)\n",
+    "\n",
+    "print(s & set(range(4,10)))           # Schnittmenge\n",
+    "print(s | set(range(4,10)))           # Vereinigungsmenge\n",
+    "print(s - set(range(4,10)))           # Differenzmenge\n",
+    "print(s ^ set(range(4,10)))           # sym. Differenzmenge (XOR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tele = {'alice': 213, 'bob': 558}            # Dictionary: Zuordnung von Keys zu Values\n",
+    "print(tele['bob'])\n",
+    "tele['charlie'] = 666                        # Einträge können hinzugefügt oder verändert werden\n",
+    "print(tele)\n",
+    "\n",
+    "d = {42: [1, 2, 3], 2.4: 31, 'valid': True}\n",
+    "print(d)                                     # Reihenfolge bleibt erhalten\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name in tele.keys():\n",
+    "    print(name)\n",
+    "print()\n",
+    "\n",
+    "for nummer in tele.values():\n",
+    "    print(nummer)\n",
+    "print()\n",
+    "\n",
+    "for name, nummer in tele.items():\n",
+    "    print(name, ': ', nummer, sep='')\n",
+    "print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "squares = [i ** 2 for i in range(-4, 5)]\n",
+    "print(squares)\n",
+    "\n",
+    "squares = []\n",
+    "for i in range(-4, 5):\n",
+    "    squares.append(i ** 2)\n",
+    "print(squares)\n",
+    "\n",
+    "squares = [i ** 2 for i in range(-4, 5) if i % 2 == 0]\n",
+    "print(squares)\n",
+    "\n",
+    "squares = []\n",
+    "for i in range(-4, 5):\n",
+    "    if i % 2 == 0:\n",
+    "        squares.append(i ** 2)\n",
+    "print(squares)\n",
+    "\n",
+    "unique_squares = {i ** 2 for i in range(-4, 5) if i % 2 == 0}\n",
+    "print(unique_squares)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print({word: len(word) for word in ('hey', 'world')})\n",
+    "\n",
+    "names = ['Alice', 'Bob', 'Charlie', 'David']\n",
+    "numbers = [333, 558, 666, 696]\n",
+    "tele = {name: no for name, no in zip(names, numbers)}\n",
+    "print(tele)\n",
+    "\n",
+    "backward_search = {no: name for name, no in tele.items()}\n",
+    "print(backward_search[666])\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/01-python-grundlagen/folien-code/folien-code.py b/01-python-grundlagen/folien-code/folien-code.py
new file mode 100644
index 0000000000000000000000000000000000000000..811f0fee09fb0c92764aa4fad829b878c1d1fc0f
--- /dev/null
+++ b/01-python-grundlagen/folien-code/folien-code.py
@@ -0,0 +1,261 @@
+# %% [markdown]
+# # Code zu Folien
+#
+# Dieses Skript bzw. Jupyter-Notebook enthält den Code, der auch auf den Folien "Python Grundlagen" enthalten ist. Zum Vorbereiten, Mitmachen oder Nacharbeiten.
+
+# %% Variablen
+a = 10                 # Datentypen werden implizit bestimmt
+print(type(a))         # type() gibt den Typ aus
+
+A = 20 / 2             # Groß- / Kleinschreibung wird unterschieden
+print(type(A))
+
+print(a ** 2, 7 // 2)  # ∗∗ ist der Potenzoperator, // steht für floor Division
+
+# %% Boolesche Operationen
+print(A == 10)                      # Vergleichsoperatoren: <, <=, >, >=, ==, !=
+print(A == 10 and a == 10)          # Verknüpfungsoperatoren: not, and, or
+print((A == 10) + 1)                # implizite Konvertierung: False → 0, True → 1
+print(isinstance(A, (int, float)))  # ist A vom Typ int oder float? wichtig um Überladung nachbilden zu können
+
+# %% Strings erstellen
+a = 'Hello'
+b = "world"
+print(a, b)  # print gibt mehrere Argumente mit Leerzeichen getrennt aus
+
+c = """Hello,
+you are my "world"!"""
+print(c)
+c = 'Hello,\nyou are my "world"!'
+print(c)
+
+# %% Stringkonkatenation und Methoden
+a = 'Hello,' + ' world'  # Leerzeichen zwischen Komma und world im String
+print(a)
+print(a + str(123))
+print(len('vier'))
+
+print(a.replace('ello', 'i'))
+print(a.lower())
+print(a.endswith('.png'))
+
+# %% Indizierung von Strings
+b = 'Beispiel'
+print(b[7], b[2], b[6], b[3])
+
+f = 'logo.pdf'
+print(f[-3] + f[-2] + f[-1])
+
+# f[0] = 'L'  # TypeError
+
+# %% Slicing von Strings
+f = 'logo.pdf'
+print(f[0:4])
+print(f[:-4], f[-3:])
+print(f[0:6:2])
+print(f[::2])
+
+# %% Beispiel für Slicing
+path = '../images/logo.pdf'
+file_pos = path.rfind('/') + 1
+print(file_pos)
+
+dir = path[:file_pos]
+file = path[file_pos:]
+print('dir:', dir, '   file:', file)
+
+# %% Ausgabe mit der print-Funktion
+print(8 + 4)
+print(8, 4)
+print(8); print(4)
+print(8, 4, 2, sep=' > ', end=' (richtige Aussage)\n\n')
+
+# %% Formatierung
+print('Hi {}!'.format(5))                        # formatierte(r) String / Ausgabe
+print('{0} {1} {0}!'.format('First', 'Things'))  # {n} referenziert Parameter n
+print('named: {test}!'.format(test=42))          # {varname} definiert Namen
+print('significant digits: {:.2}'.format(20/3))  # {...:.2} 2 signifikante Stellen
+print('Fill: {:04}'.format(3))                   # {...:0s} reserviert s Zeichen und füllt mit 0
+print('Fixed point: {:.2f}'.format(3))           # {...:f} gibt Zahl als fixed point aus
+
+# %% f-Strings und alter Formatierungsstil
+
+print(f'Explicit: {3:.2f}')            # f'\{var:...\}' ersetzt '{:...}'.format(var)
+print('old style: %d %.2f' % (2, 3))   # formatierte(r) String / Ausgabe
+print('bad style: %d %d' % (2.3, 3.8)) # Typspezifizierer könnten falsch sein! ⚡
+
+ip = '127.0.0.1'
+port = 8888
+# port = '8888'  # ergibt Fehler beim alten Formatierungsstil.
+server = ip + ':%d' % port                 # Wurde port vielleicht als String gegeben? ⚡
+print(server)
+server = f'{ip}:{port}'                    # Besser! Funktioniert mit strings und ints 👍
+print(server)
+
+# %% Verzweigung
+if A > 10:
+    print('too big')
+elif A > 5:             # es kann mehrere elif-Zweige geben
+    print('correct')
+else:
+    print('too small')
+print()
+
+if 's' in 'Hellas':
+    print('It is')
+
+# %% for-Schleifen
+for c in 'bla':
+    print('Buchstabe:', c)
+print()
+
+for i in range(3):
+    print('It:', i)
+print()
+
+for i, c in enumerate('bla'):
+    print('It:', i, 'Buchstabe:', c)
+print()
+
+for c, s in zip('ABC', 'abc'):
+    print(c + s)
+
+# %% break und continue und while-Schleifen
+for c in 'AKIS':
+    if c == 'I':
+        break
+    print(c)
+print()
+
+for c in 'AKIS':
+    if c == 'I':
+        continue
+    print(c)
+print()
+
+while True:
+    prompt = input('> ')
+    if prompt == 'exit':
+        break
+    print(prompt)
+
+# %% Funktionen definieren und aufrufen
+def greet(name):
+    print('Hello', name)
+
+greet('and goodbye!')    # Ausgabe: Hello and goodbye!
+
+def greet(name='my sunshine!'):
+    print('Hello', name)
+
+greet()                  # Ausgabe: Hello my sunshine!
+greet('you')             # Ausgabe: Hello you
+
+# %% Rückgabewerte und benannte Argumente
+def f(x, y):
+    return x**2 - y**2
+
+print(f(1, 2))
+print(f(y=2, x=1))  # Reihenfolge ist egal
+
+def f(x=0, y=0):
+    return x**2 - y**2
+
+print(f(y=2))       # x=0, y=2
+
+# %% Mehrere Rückgabewerte
+import math
+def pq_formel(p, q):
+    x1 = -p / 2 + math.sqrt(p**2 / 4 - q)
+    x2 = -p / 2 - math.sqrt(p**2 / 4 - q)
+    return x1, x2
+
+lsg1, lsg2 = pq_formel(2, 0)  # Ausgabe:
+print('Lösungen:')            # Lösungen:
+print('x1 =', lsg1)           # x1 = 0.0
+print('x2 =', lsg2)           # x2 = -2.0
+
+# %% Listen
+liste = [1, 'text', 1/3]          # Listenelemente können verschiede Typen haben
+liste[0] = 2                      # Listen sind veränderbar
+liste.append(42)                  # Listen sind erweiterbar
+print(liste)
+
+liste = ['tag', 'monat', 'jahr']
+print(liste.index('monat'))       # Stelle bzw. Index des Elements 'monat'
+
+liste = [1, 2, 3] * 2             # Ergibt: liste = [1, 2, 3, 1, 2, 3]
+print(liste)
+
+# %% Tupel
+t = (3, 'text')               # Tupel-Elemente können auch verschiedene Typen haben
+print(t[1])                   # Lesen geht
+# t[1] = 5                    # Schreiben nicht (TypeError)
+
+print((3, 4) + (6, 8))        # Ergibt: (3, 4, 6, 8)
+
+c, d = 5, 7                   # c ist 5 und d ist 7 (``unpacking''), 5, 7 ist hier ein Tupel
+print(c, d)
+
+# %% Mengen
+s = {11, 7, 3, 13, 2, 6}         # #*set* wird definiert mit #*\{...\}*
+print(s)
+
+print(s & set(range(4,10)))           # Schnittmenge
+print(s | set(range(4,10)))           # Vereinigungsmenge
+print(s - set(range(4,10)))           # Differenzmenge
+print(s ^ set(range(4,10)))           # sym. Differenzmenge (XOR)
+
+# %% Dictionaries
+tele = {'alice': 213, 'bob': 558}            # Dictionary: Zuordnung von Keys zu Values
+print(tele['bob'])
+tele['charlie'] = 666                        # Einträge können hinzugefügt oder verändert werden
+print(tele)
+
+d = {42: [1, 2, 3], 2.4: 31, 'valid': True}
+print(d)                                     # Reihenfolge bleibt erhalten
+
+# %% Schleifen über Dictionaries
+for name in tele.keys():
+    print(name)
+print()
+
+for nummer in tele.values():
+    print(nummer)
+print()
+
+for name, nummer in tele.items():
+    print(name, ': ', nummer, sep='')
+print()
+
+# %% List und Set Comprehension
+squares = [i ** 2 for i in range(-4, 5)]
+print(squares)
+
+squares = []
+for i in range(-4, 5):
+    squares.append(i ** 2)
+print(squares)
+
+squares = [i ** 2 for i in range(-4, 5) if i % 2 == 0]
+print(squares)
+
+squares = []
+for i in range(-4, 5):
+    if i % 2 == 0:
+        squares.append(i ** 2)
+print(squares)
+
+unique_squares = {i ** 2 for i in range(-4, 5) if i % 2 == 0}
+print(unique_squares)
+
+# %% Dictionary Comprehension
+print({word: len(word) for word in ('hey', 'world')})
+
+names = ['Alice', 'Bob', 'Charlie', 'David']
+numbers = [333, 558, 666, 696]
+tele = {name: no for name, no in zip(names, numbers)}
+print(tele)
+
+backward_search = {no: name for name, no in tele.items()}
+print(backward_search[666])
diff --git a/03-numpy-und-matplotlib/06-hidden-message-sol.ipynb b/03-numpy-und-matplotlib/06-hidden-message-sol.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..09ddef1a7049ab2f860fd58e070c474ec82332c4
--- /dev/null
+++ b/03-numpy-und-matplotlib/06-hidden-message-sol.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Information im Rauschen\n",
+    "\n",
+    "Man kann Bilder so manipulieren, dass sie Informationen enthalten, die\n",
+    "man beim Betrachten höchstens als Rauschen wahrnehmen kann.\n",
+    "\n",
+    "<figure>\n",
+    "<figure>\n",
+    "<img\n",
+    "src=\"data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
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\"\n",
+    "alt=\"Prinzipbild einer in einem Bild versteckten Nachricht mit einem Bit pro Farbwert\" />\n",
+    "<figcaption aria-hidden=\"true\">Prinzipbild einer in einem Bild\n",
+    "versteckten Nachricht mit einem Bit pro Farbwert</figcaption>\n",
+    "</figure>\n",
+    "<figcaption>Prinzipbild einer in einem Bild versteckten Nachricht mit\n",
+    "einem Bit pro Farbwert</figcaption>\n",
+    "</figure>\n",
+    "\n",
+    "Versuchen Sie aus dem Bild `zwinkersmiley.bmp` eine Nachricht zu\n",
+    "extrahieren und sie auszugeben! Das Bild wird schon als 3D-NumPy-Array\n",
+    "geladen (Höhe x Breite x Kanäle)."
+   ],
+   "id": "0003-aaa5ddb6637d60757bd46091f1a2eca1cc7f981da853b1aa1465e25d06b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img = plt.imread('zwinkersmiley.bmp')"
+   ],
+   "id": "0004-8154792f1920f755c9dc3fb7a02c024aa0338feb6e6a9a02bc89046131b"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Bonus: Schaffen Sie es auch umgekehrt, also eine Nachricht in einem\n",
+    "Bild zu verstecken?*\n",
+    "\n",
+    "*Hinweis: Folgende Funktionen bzw. Methoden könnten nützlich sein:*\n",
+    "\n",
+    "-   [`np.ravel`](https://numpy.org/doc/stable/reference/generated/numpy.ravel.html)\n",
+    "    formt ein Array in 1D um.\n",
+    "-   [`np.reshape`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy.reshape)\n",
+    "    bringt ein Array in eine beliebige Form, z. B. auch in 1D.\n",
+    "-   [`np.astype`](https://numpy.org/doc/stable/reference/generated/numpy.astype.html#numpy-astype)\n",
+    "    ändert den Typ eines Arrays, z. B. ein Array mit `int`s in `str`ings\n",
+    "    oder in `'uint8'`.\n",
+    "-   [`np.resize`](https://numpy.org/doc/stable/reference/generated/numpy.resize.html#numpy-resize)\n",
+    "    erweitert ein Array auf eine bestimmte Größe und füllt es ggf. mit\n",
+    "    0en auf.\n",
+    "-   [`np.tile`](https://numpy.org/doc/stable/reference/generated/numpy.tile.html#numpy-tile)\n",
+    "    pflastert ein Array eine bestimmte Anzahl aneinander, z. B. erzeugt\n",
+    "    np.tile(\\[1, 2, 3\\], reps=\\[2, 1\\]) ein Array mit zwei Reihen \\[1,\n",
+    "    2, 3\\].\n",
+    "-   [`np.sum`](https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy-sum)\n",
+    "    summert ein Array entweder komplett oder entlang vorgegebner Achsen\n",
+    "    auf, z. B. erzeugt np.sum(a, axis=1) die Zeilensummen eines\n",
+    "    2D-Arrays `a`.\n",
+    "-   [`np.fromstring`](https://numpy.org/doc/stable/reference/generated/numpy.fromstring.html#numpy-fromstring)\n",
+    "    erzeugt ein NumPy-Array aus einem String, z. B.\n",
+    "    `np.fromstring('1,2,3', dtype=int, sep=',')`\n",
+    "-   [`np.full`](https://numpy.org/doc/stable/reference/generated/numpy.full.html#numpy.full)\n",
+    "    erzeugt ein Array mit einem bestimmten Wert, Typ und Größe.\n",
+    "-   [`str.join`](https://docs.python.org/3/library/stdtypes.html#str.join)\n",
+    "    erlaubt eine Liste mit Strings zu vereinen, z. B. ohne Trenner mit\n",
+    "    `''.join(['b', 'e']) == 'be'`\n",
+    "-   [`range`](https://docs.python.org/3/library/stdtypes.html#range) zum\n",
+    "    Iterieren mit `range(start, stop, step)`\n",
+    "-   [`format`](https://docs.python.org/3/library/functions.html#format)\n",
+    "    wandelt z. B. eine Ganzzahl in einen String entsprechend dem\n",
+    "    Formatspezifizierer um, z. B. `format(5, '08b') == '00000101'`\n",
+    "-   [`int`](https://docs.python.org/3/library/functions.html#int)\n",
+    "    wandelt einen String in eine Ganzzahl um, z. B. `int('5') == 5` oder\n",
+    "    mit `base=2` in Binärschreibweise: `int('101', base=2) == 5`.\n",
+    "-   [`chr`](https://docs.python.org/3/library/functions.html#chr)\n",
+    "    wandelt eine Ganzzahl in ein Zeichen um, z. B. `chr(101) == 'e'`\n",
+    "-   [`ord`](https://docs.python.org/3/library/functions.html#ord)\n",
+    "    wandelt ein Zeichen in eine Ganzzahl um, z. B. `ord('e') == 101`\n",
+    "\n",
+    "## Lösung\n",
+    "\n",
+    "Zunächst machen wir aus dem 3D-NumPy-Array mittels `ravel` ein\n",
+    "1D-NumPy-Array. Anschließend sind wir an dem jeweils letzten Bit\n",
+    "interessiert. Das bekommen wir ganz einfach indem wir Modulo 2 (Restwert\n",
+    "der Division mit 2) des Arrays berechnen. Für gerade Zahlen ist der Rest\n",
+    "0 für ungerade 1."
+   ],
+   "id": "0009-b75d348f38b280c8c55fe4d45568a528fff1d6a5a2cadd717770563464f"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "noise = img.ravel() % 2"
+   ],
+   "id": "0010-e7b7b5fc735282ad62b19bd1dd49586d2badba14201e8892f69dc74b210"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jetzt haben wir ein 1D-NumPy-Array mit 0en und 1en. Man kann von hier\n",
+    "sicherlich auf vielerlei Art weiter kommen. Wir wandeln die Zahlen in\n",
+    "ihre String-Repräsentation um und führen Sie dann zu einem langen String\n",
+    "aus 0en und 1en zusammen."
+   ],
+   "id": "0011-f2af408fef45d452cf03b6d6350eb996166adff3483f48e8359b3b46753"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "stream = ''.join(noise.astype(str))"
+   ],
+   "id": "0012-e8769af5f0f93b3d790101ef11b2f35ea21b325ff38815ebcd95b599854"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Als nächstes trennen wir den String per Slicing in (max.) 8er-Ketten und\n",
+    "wandeln diese in Ganzzahlen um."
+   ],
+   "id": "0013-5d8bed03128d27cfac6b23b3a86e06ddd10625b715c3526d1a204e3646b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "ints = [int(stream[start:start + 8], base=2) for start in range(0, len(stream) - 8, 8)]"
+   ],
+   "id": "0014-67ba02814a1f1e523f41beb62ed02df65f0643041dc2a73023a4a168abb"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jede Ganzzahl, die nicht 0 ist, wandeln wir als nächstes mit `chr` zu\n",
+    "einem Zeichen um und diese führen wir zu einem String – der versteckten\n",
+    "Nachricht – zusammen."
+   ],
+   "id": "0015-d39a8023abaeed6595c34bbe08c8e78cbbf35b47f8f5fab3f9664d3d2d9"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "chars = [chr(i) for i in ints if i]\n",
+    "msg = ''.join(chars)"
+   ],
+   "id": "0016-21242ff5249eb22c1ce72b43f4f9d24512b7f2f3d928213c7d8d4098948"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Yay! Sie haben die Aufgabe geschafft. War gar nicht so einfach, oder? Naja... viel Spaß noch mit den anderen Aufgaben! ;)"
+     ]
+    }
+   ],
+   "source": [
+    "print(msg)"
+   ],
+   "id": "0017-2417d4e0a8b2829fba586d0a4c0d5ae48b73fc7ffd6f4bf6da7324894af"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hier noch eine Alternative Lösung, die erst jeweils 8 0en und 1en in\n",
+    "eine Zeile schreibt. Dabei wird `resize` gebraucht, falls die Länge\n",
+    "nicht durch 8 teilbar ist. So werden fehlende Werte mit 0 aufgefüllt.\n",
+    "Das Array wird dann mit den Bitwertigkeiten (128, 64, 32, 16, 8, 4,\n",
+    "2, 1) multipliziert. Anschließend werden die Zeilensummen gebildet um\n",
+    "die Ganzzahlen zu erhalten. Es kann sein, dass am Ende nur 0en stehen,\n",
+    "um auf Bildgröße aufzufüllen, daher entfernen wir 0en. Das sind ohnehin\n",
+    "keine gültigen Zeichen. Die Zahlen werden dann mittels `chr` in Zeichen\n",
+    "umgewandelt und zu einem String zusammengefügt."
+   ],
+   "id": "0018-b2c2f1aa5ab516501c202b92deac6c65ed8e7b622c3e112e7e423d85619"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "noise.resize((int(np.ceil(noise.size / 8)), 8))\n",
+    "bit_values = noise * 2 ** np.arange(7, -1, -1)\n",
+    "ints = np.sum(bit_values, axis=1)\n",
+    "ints = ints[ints != 0]\n",
+    "msg = ''.join(map(chr, ints))"
+   ],
+   "id": "0019-8d445a46f0cea5dba0c3730fe53ecdaebdc8bf826467df070f406447057"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/03-numpy-und-matplotlib/06-hidden-message.ipynb b/03-numpy-und-matplotlib/06-hidden-message.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d4c56511b7f26bfb39c028777a2fa97640ea696c
--- /dev/null
+++ b/03-numpy-und-matplotlib/06-hidden-message.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Information im Rauschen\n",
+    "\n",
+    "Man kann Bilder so manipulieren, dass sie Informationen enthalten, die\n",
+    "man beim Betrachten höchstens als Rauschen wahrnehmen kann.\n",
+    "\n",
+    "<figure>\n",
+    "<figure>\n",
+    "<img\n",
+    "src=\"data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
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\"\n",
+    "alt=\"Prinzipbild einer in einem Bild versteckten Nachricht mit einem Bit pro Farbwert\" />\n",
+    "<figcaption aria-hidden=\"true\">Prinzipbild einer in einem Bild\n",
+    "versteckten Nachricht mit einem Bit pro Farbwert</figcaption>\n",
+    "</figure>\n",
+    "<figcaption>Prinzipbild einer in einem Bild versteckten Nachricht mit\n",
+    "einem Bit pro Farbwert</figcaption>\n",
+    "</figure>\n",
+    "\n",
+    "Versuchen Sie aus dem Bild `zwinkersmiley.bmp` eine Nachricht zu\n",
+    "extrahieren und sie auszugeben! Das Bild wird schon als 3D-NumPy-Array\n",
+    "geladen (Höhe x Breite x Kanäle)."
+   ],
+   "id": "0003-aaa5ddb6637d60757bd46091f1a2eca1cc7f981da853b1aa1465e25d06b"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "style": "python"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img = plt.imread('zwinkersmiley.bmp')"
+   ],
+   "id": "0004-8154792f1920f755c9dc3fb7a02c024aa0338feb6e6a9a02bc89046131b"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Bonus: Schaffen Sie es auch umgekehrt, also eine Nachricht in einem\n",
+    "Bild zu verstecken?*\n",
+    "\n",
+    "*Hinweis: Folgende Funktionen bzw. Methoden könnten nützlich sein:*\n",
+    "\n",
+    "-   [`np.ravel`](https://numpy.org/doc/stable/reference/generated/numpy.ravel.html)\n",
+    "    formt ein Array in 1D um.\n",
+    "-   [`np.reshape`](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy.reshape)\n",
+    "    bringt ein Array in eine beliebige Form, z. B. auch in 1D.\n",
+    "-   [`np.astype`](https://numpy.org/doc/stable/reference/generated/numpy.astype.html#numpy-astype)\n",
+    "    ändert den Typ eines Arrays, z. B. ein Array mit `int`s in `str`ings\n",
+    "    oder in `'uint8'`.\n",
+    "-   [`np.resize`](https://numpy.org/doc/stable/reference/generated/numpy.resize.html#numpy-resize)\n",
+    "    erweitert ein Array auf eine bestimmte Größe und füllt es ggf. mit\n",
+    "    0en auf.\n",
+    "-   [`np.tile`](https://numpy.org/doc/stable/reference/generated/numpy.tile.html#numpy-tile)\n",
+    "    pflastert ein Array eine bestimmte Anzahl aneinander, z. B. erzeugt\n",
+    "    np.tile(\\[1, 2, 3\\], reps=\\[2, 1\\]) ein Array mit zwei Reihen \\[1,\n",
+    "    2, 3\\].\n",
+    "-   [`np.sum`](https://numpy.org/doc/stable/reference/generated/numpy.sum.html#numpy-sum)\n",
+    "    summert ein Array entweder komplett oder entlang vorgegebner Achsen\n",
+    "    auf, z. B. erzeugt np.sum(a, axis=1) die Zeilensummen eines\n",
+    "    2D-Arrays `a`.\n",
+    "-   [`np.fromstring`](https://numpy.org/doc/stable/reference/generated/numpy.fromstring.html#numpy-fromstring)\n",
+    "    erzeugt ein NumPy-Array aus einem String, z. B.\n",
+    "    `np.fromstring('1,2,3', dtype=int, sep=',')`\n",
+    "-   [`np.full`](https://numpy.org/doc/stable/reference/generated/numpy.full.html#numpy.full)\n",
+    "    erzeugt ein Array mit einem bestimmten Wert, Typ und Größe.\n",
+    "-   [`str.join`](https://docs.python.org/3/library/stdtypes.html#str.join)\n",
+    "    erlaubt eine Liste mit Strings zu vereinen, z. B. ohne Trenner mit\n",
+    "    `''.join(['b', 'e']) == 'be'`\n",
+    "-   [`range`](https://docs.python.org/3/library/stdtypes.html#range) zum\n",
+    "    Iterieren mit `range(start, stop, step)`\n",
+    "-   [`format`](https://docs.python.org/3/library/functions.html#format)\n",
+    "    wandelt z. B. eine Ganzzahl in einen String entsprechend dem\n",
+    "    Formatspezifizierer um, z. B. `format(5, '08b') == '00000101'`\n",
+    "-   [`int`](https://docs.python.org/3/library/functions.html#int)\n",
+    "    wandelt einen String in eine Ganzzahl um, z. B. `int('5') == 5` oder\n",
+    "    mit `base=2` in Binärschreibweise: `int('101', base=2) == 5`.\n",
+    "-   [`chr`](https://docs.python.org/3/library/functions.html#chr)\n",
+    "    wandelt eine Ganzzahl in ein Zeichen um, z. B. `chr(101) == 'e'`\n",
+    "-   [`ord`](https://docs.python.org/3/library/functions.html#ord)\n",
+    "    wandelt ein Zeichen in eine Ganzzahl um, z. B. `ord('e') == 101`"
+   ],
+   "id": "0007-5c6af64a90afb5b6d43c61a21161b2765e2e9fb69946f2e1d54ae72b91c"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/03-numpy-und-matplotlib/zwinkersmiley.bmp b/03-numpy-und-matplotlib/zwinkersmiley.bmp
new file mode 100644
index 0000000000000000000000000000000000000000..c0ea1d58a71f19cdfed951285220d368a7dcb8c2
Binary files /dev/null and b/03-numpy-und-matplotlib/zwinkersmiley.bmp differ
diff --git a/08-korrelation-und-dimensionsreduktion/05-reduce-mnist-sol.ipynb b/08-korrelation-und-dimensionsreduktion/05-reduce-mnist-sol.ipynb
index 24d953be8c6f080985fd3c0dd572be6b31500fe5..917fe92abd77dd96c9bf490eaf7948c1b45b81ff 100644
--- a/08-korrelation-und-dimensionsreduktion/05-reduce-mnist-sol.ipynb
+++ b/08-korrelation-und-dimensionsreduktion/05-reduce-mnist-sol.ipynb
@@ -8,11 +8,18 @@
     "\n",
     "In dieser Aufgabe wollen wir einen hochdimensionalen Datensatz in 2D\n",
     "(oder 3D) plotten. Die Daten werden schon geladen und eine\n",
-    "Plotting-Funktion ist schon vorbereitet. *Hinweis: Der Code benötigt\n",
-    "einen Jupyter-Kontext, also Jupyter Lab oder VS Code mit interaktivem\n",
-    "Modus.*"
+    "Plotting-Funktion ist schon vorbereitet.\n",
+    "\n",
+    "*Hinweis: Sie benötigen `bokeh`, was Sie einfach mit\n",
+    "`mamba install bokeh` installieren können.*\n",
+    "\n",
+    "*Hinweis: Der vorbereitete Code benötigt aktuell einen Jupyter-Kontext,\n",
+    "also Jupyter Lab oder VS Code mit interaktivem Modus. Wenn Sie den Code\n",
+    "im Script-Modus nutzen wollen, müssen Sie den Code oder mit dem\n",
+    "auskommentierten Code, der mit `bokeh.plotting.output_file` anfängt,\n",
+    "dekommentieren.*"
    ],
-   "id": "0001-d947a237f8749d5a07e6b16afc9277567861c9725260e81c2bffa0ec68d"
+   "id": "0003-7f7af66911fa1dbbd53a09cf8eb63d7bbae02ca052ea151d8b799a02d83"
   },
   {
    "cell_type": "code",
@@ -22,64 +29,93 @@
    },
    "outputs": [],
    "source": [
+    "import base64\n",
     "import io\n",
-    "import matplotlib.pyplot as plt\n",
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import PIL\n",
     "import plotly.express as px\n",
-    "from plotly import graph_objects as go\n",
-    "from ipywidgets import VBox, Box, Image, Layout\n",
-    "from tensorflow.keras.datasets.mnist import load_data\n",
-    "\n",
-    "\n",
-    "def interactive_scatter_plot(X_high, X_low, y):\n",
-    "    \"\"\"\n",
-    "    Make a scatter plot reacting on hover by showing the image\n",
-    "    \"\"\"\n",
-    "    assert X_high.shape[0] == X_low.shape[0] == y.shape[0], 'Arrays should have the same number of samples'\n",
-    "    assert X_high.shape[1] > X_low.shape[1], 'First array should bei original images, second with reduced dimension'\n",
-    "\n",
-    "    x_name, y_name = 0, 1 if not isinstance(X_low, pd.DataFrame) else X_low.columns[:2]\n",
-    "    scatter = px.scatter(X_low, x=x_name, y=y_name, color=y, hover_data={'idx': np.arange(len(X_low))})\n",
-    "    scatter.update_xaxes(title_text=None)\n",
-    "    scatter.update_yaxes(title_text=None)\n",
-    "\n",
-    "    # show image on hover\n",
-    "    img = Image(format='png', width=56)\n",
-    "    def update(trace, points, state):\n",
-    "        # index relative to this trace (trace = color group)\n",
-    "        trace_index = points.point_inds\n",
-    "        if len(trace_index) == 0:\n",
-    "            # this returns for traces not having the data point\n",
-    "            return\n",
+    "from keras.datasets.mnist import load_data\n",
+    "import bokeh\n",
+    "import bokeh.plotting\n",
+    "import bokeh.models\n",
+    "import bokeh.palettes\n",
     "\n",
-    "        # absolute indices of this trace\n",
-    "        digit_indices = trace['customdata']\n",
-    "        data_index = digit_indices[trace_index]\n",
+    "title = 'MNIST 2D'\n",
     "\n",
-    "        # convert image to PNG bytes and set image to it\n",
-    "        rawBytes = io.BytesIO()\n",
-    "        plt.imsave(rawBytes, X_high[data_index].reshape((28, 28)), format='png', cmap='gray')\n",
-    "        rawBytes.seek(0)\n",
-    "        img.value = rawBytes.read()\n",
+    "# either INTERACTIVE MODE: directly show in interactive window / notebook\n",
+    "bokeh.plotting.output_notebook()\n",
+    "# or SCRIPT MODE: output to file and open in browser\n",
+    "# filename = os.path.join('images', 'mnist2d.html')\n",
+    "# bokeh.plotting.output_file(filename=filename, title=title)\n",
     "\n",
-    "    fig = go.FigureWidget(data=scatter.data, layout=scatter.layout)\n",
     "\n",
-    "    # figure contains for each color a data trace and for each we have to define the on_hover function\n",
-    "    for fig_data in fig.data:\n",
-    "        fig_data.on_hover(update)\n",
+    "def img_to_base64(img_array):\n",
+    "    assert np.issubdtype(img_array.dtype, 'uint8'), 'Pillow expects an image array to have uint8 format'\n",
+    "    img = PIL.Image.fromarray(img_array.squeeze())\n",
+    "    buffer = io.BytesIO()\n",
+    "    img.save(buffer, format='WebP')\n",
+    "    return 'data:image/webp;base64,' + base64.b64encode(buffer.getvalue()).decode()\n",
     "\n",
-    "    # layout of plot and centered image\n",
-    "    return VBox([fig, Box([img], layout=Layout(display='flex', flex_flow='column', align_items='center'))])\n",
     "\n",
+    "def interactive_scatter_plot(X_high, X_low, y_int):\n",
+    "    \"\"\"\n",
+    "    Make a scatter plot reacting on hover by showing the image\n",
+    "    \"\"\"\n",
+    "    assert X_high.ndim >= 3, 'Original images should be shaped as images e. g. (n_samples, height, width)'\n",
+    "    assert X_low.ndim == 2, 'Reduced dimension should be 2D (n_samples, n_components)'\n",
+    "    assert X_high.shape[0] == X_low.shape[0] == y_int.shape[0], 'Arrays should have the same number of samples'\n",
+    "    assert np.prod(X_high.shape[1:]) > X_low.shape[1], 'First array should be original images, second with reduced dimension'\n",
+    "\n",
+    "    if not isinstance(X_low, pd.DataFrame):\n",
+    "        X_low = pd.DataFrame(X_low, columns=['x', 'y'])\n",
+    "    X_low['class'] = y_int.astype(str)\n",
+    "    X_low['image'] = [img_to_base64(x) for x in X_high]\n",
+    "\n",
+    "    datasource = bokeh.models.ColumnDataSource(X_low)\n",
+    "    color_mapping = bokeh.models.CategoricalColorMapper(\n",
+    "        factors=np.unique(X_low['class']),\n",
+    "        palette=bokeh.palettes.Spectral[X_low['class'].nunique()]\n",
+    "    )\n",
+    "\n",
+    "    plot_figure = bokeh.plotting.figure(\n",
+    "        title=title,\n",
+    "        width=1000,\n",
+    "        height=1000,\n",
+    "        tools=('pan, wheel_zoom, reset, zoom_in')\n",
+    "    )\n",
+    "\n",
+    "    plot_figure.add_tools(bokeh.models.HoverTool(tooltips=\"\"\"\n",
+    "    <div>\n",
+    "        <div>\n",
+    "            <img src='@image' width='96' style='float: left; margin: 5px 5px 5px 5px'/>\n",
+    "        </div>\n",
+    "        <div>\n",
+    "            <span style='font-size: 16px; color: #224499'>Class:</span>\n",
+    "            <span style='font-size: 18px'>@class</span>\n",
+    "        </div>\n",
+    "    </div>\n",
+    "    \"\"\"))\n",
+    "\n",
+    "    plot_figure.scatter(\n",
+    "        'x',\n",
+    "        'y',\n",
+    "        source=datasource,\n",
+    "        color=dict(field='class', transform=color_mapping),\n",
+    "        line_alpha=0.6,\n",
+    "        fill_alpha=0.6,\n",
+    "        size=4\n",
+    "    )\n",
+    "    return plot_figure\n",
     "\n",
     "# load data\n",
-    "(x_train, y_train), (x_test, y_test) = load_data()\n",
+    "(X_train, y_train_int), (X_test, y_test_int) = load_data()\n",
     "\n",
     "# reshape test set to 10 000 x 784\n",
-    "X = x_test.reshape(-1, 28 * 28)"
+    "X = X_test.reshape(-1, 28 * 28)"
    ],
-   "id": "0002-bbdfcc807a677cb51c1861eff7e69e8889bcc9c56d11ca63b972d0d11c7"
+   "id": "0004-5dcaf1cd1786238c4749189d6237574dbb5addb57128548dbe84d900608"
   },
   {
    "cell_type": "markdown",
@@ -88,7 +124,7 @@
     "Wenn Sie mögen ist hier ein Plot von verschiedenen Bildern der gleichen\n",
     "Klasse. So können Sie ein Blick in den Datensatz werfen."
    ],
-   "id": "0003-8683c8202e9f2668b92e0aa78e49a38198e4d638b65ac30ec6020531315"
+   "id": "0005-8683c8202e9f2668b92e0aa78e49a38198e4d638b65ac30ec6020531315"
   },
   {
    "cell_type": "code",
@@ -101,21 +137,21 @@
     "# plot 50 examples for each digit\n",
     "imgs = np.empty((50, 10, 28, 28))\n",
     "for j in range(10):\n",
-    "    imgs[:, j] = x_test[y_test == j][:50]\n",
+    "    imgs[:, j] = X_test[y_test_int == j][:50]\n",
     "\n",
     "fig = px.imshow(imgs, animation_frame=0, facet_col=1, facet_col_wrap=5, binary_string=True)\n",
     "fig.update_xaxes(showticklabels=False)\n",
     "fig.update_yaxes(showticklabels=False)\n",
     "fig.show()"
    ],
-   "id": "0004-c879d58b500c83a0364d8680cc6b05c9cc97d36d3ea3743e112c56644db"
+   "id": "0006-55d5e2344dc9d02ca2bfa1b0c375229ad58016396aa7d7cf5a61050d2d3"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Transformieren Sie die Daten in 2D (ode 3D) und plotten die\n",
-    "Transformierten Daten als Scatter-Plot mit `y_test` als\n",
+    "Transformieren Sie die Daten in 2D (oder 3D) und plotten die\n",
+    "transformierten Daten als Scatter-Plot mit `y_test_int` als\n",
     "Farbunterscheidung. Für PCA ist es schon vorbereitet, aber probieren Sie\n",
     "auch andere Techniken.\n",
     "\n",
@@ -124,7 +160,7 @@
     "mit `from umap import UMAP` importieren und wie jedes andere\n",
     "Scikit-Learn-Modell verwenden.*"
    ],
-   "id": "0006-80f9722cacf3b6426928b4bbdb5fcb32d9d50cc0aaccb011c37c93b7f19"
+   "id": "0008-a567fb8b0d99e19468231f138dc3f8c24df37b96f4a7e1ef8fee53d2cd8"
   },
   {
    "cell_type": "code",
@@ -139,10 +175,10 @@
     "pca = PCA(n_components=2)\n",
     "X_pca = pca.fit_transform(X)\n",
     "\n",
-    "plot = interactive_scatter_plot(X, X_pca, y_test)\n",
-    "display(plot)  # display from IPython.display implicitly in Jupyter imported, which is required anyway"
+    "fig = interactive_scatter_plot(X_test, X_pca, y_test_int)\n",
+    "bokeh.plotting.show(fig)"
    ],
-   "id": "0007-ae5f11cf01a3212244c7bf86638455d0d6a145258c3112beb4c12633405"
+   "id": "0009-8f4cb7ad5313b8b11707eac44fb277bcac2da77b123fc2d06542453784b"
   },
   {
    "cell_type": "markdown",
@@ -150,9 +186,9 @@
    "source": [
     "## Lösung\n",
     "\n",
-    "Hier der Code für UMAP:"
+    "Hier der Code für UMAP (Ausführung dauert etwas länger, ca. 1 min):"
    ],
-   "id": "0009-96b725bb9f2f51ff66328e5e9a5fc2dd4e69320f643fa34ea6db4da4c4a"
+   "id": "0011-2ea29f1d1ac0466469de63febbc098721eca721cd7a2798db189d242db7"
   },
   {
    "cell_type": "code",
@@ -167,10 +203,10 @@
     "umap = UMAP(n_neighbors=20, metric='manhattan', min_dist=0.1)\n",
     "X_umap = umap.fit_transform(X)\n",
     "\n",
-    "plot = interactive_scatter_plot(X, X_umap, y_test)\n",
-    "display(plot)  # display from IPython.display implicitly in Jupyter imported, which is required anyway"
+    "fig = interactive_scatter_plot(X_test, X_umap, y_test_int)\n",
+    "bokeh.plotting.show(fig)"
    ],
-   "id": "0010-7e298e46cd6eb14f0920a2bed338fa02b756553adb499afb357944d32ae"
+   "id": "0012-45ec0cf146b8998fa17e4fe7c5ae1bacbf4ab8c523e43e32e1233c63ecb"
   }
  ],
  "nbformat": 4,
diff --git a/08-korrelation-und-dimensionsreduktion/05-reduce-mnist.ipynb b/08-korrelation-und-dimensionsreduktion/05-reduce-mnist.ipynb
index 8ab188aec81f6c2a7d213ad7d22e9043813428b5..750dbc8a2a0f9c985e7d958cdc47c0e9dc9ab751 100644
--- a/08-korrelation-und-dimensionsreduktion/05-reduce-mnist.ipynb
+++ b/08-korrelation-und-dimensionsreduktion/05-reduce-mnist.ipynb
@@ -8,11 +8,18 @@
     "\n",
     "In dieser Aufgabe wollen wir einen hochdimensionalen Datensatz in 2D\n",
     "(oder 3D) plotten. Die Daten werden schon geladen und eine\n",
-    "Plotting-Funktion ist schon vorbereitet. *Hinweis: Der Code benötigt\n",
-    "einen Jupyter-Kontext, also Jupyter Lab oder VS Code mit interaktivem\n",
-    "Modus.*"
+    "Plotting-Funktion ist schon vorbereitet.\n",
+    "\n",
+    "*Hinweis: Sie benötigen `bokeh`, was Sie einfach mit\n",
+    "`mamba install bokeh` installieren können.*\n",
+    "\n",
+    "*Hinweis: Der vorbereitete Code benötigt aktuell einen Jupyter-Kontext,\n",
+    "also Jupyter Lab oder VS Code mit interaktivem Modus. Wenn Sie den Code\n",
+    "im Script-Modus nutzen wollen, müssen Sie den Code oder mit dem\n",
+    "auskommentierten Code, der mit `bokeh.plotting.output_file` anfängt,\n",
+    "dekommentieren.*"
    ],
-   "id": "0001-d947a237f8749d5a07e6b16afc9277567861c9725260e81c2bffa0ec68d"
+   "id": "0003-7f7af66911fa1dbbd53a09cf8eb63d7bbae02ca052ea151d8b799a02d83"
   },
   {
    "cell_type": "code",
@@ -22,64 +29,93 @@
    },
    "outputs": [],
    "source": [
+    "import base64\n",
     "import io\n",
-    "import matplotlib.pyplot as plt\n",
+    "import os\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import PIL\n",
     "import plotly.express as px\n",
-    "from plotly import graph_objects as go\n",
-    "from ipywidgets import VBox, Box, Image, Layout\n",
-    "from tensorflow.keras.datasets.mnist import load_data\n",
-    "\n",
-    "\n",
-    "def interactive_scatter_plot(X_high, X_low, y):\n",
-    "    \"\"\"\n",
-    "    Make a scatter plot reacting on hover by showing the image\n",
-    "    \"\"\"\n",
-    "    assert X_high.shape[0] == X_low.shape[0] == y.shape[0], 'Arrays should have the same number of samples'\n",
-    "    assert X_high.shape[1] > X_low.shape[1], 'First array should bei original images, second with reduced dimension'\n",
-    "\n",
-    "    x_name, y_name = 0, 1 if not isinstance(X_low, pd.DataFrame) else X_low.columns[:2]\n",
-    "    scatter = px.scatter(X_low, x=x_name, y=y_name, color=y, hover_data={'idx': np.arange(len(X_low))})\n",
-    "    scatter.update_xaxes(title_text=None)\n",
-    "    scatter.update_yaxes(title_text=None)\n",
-    "\n",
-    "    # show image on hover\n",
-    "    img = Image(format='png', width=56)\n",
-    "    def update(trace, points, state):\n",
-    "        # index relative to this trace (trace = color group)\n",
-    "        trace_index = points.point_inds\n",
-    "        if len(trace_index) == 0:\n",
-    "            # this returns for traces not having the data point\n",
-    "            return\n",
+    "from keras.datasets.mnist import load_data\n",
+    "import bokeh\n",
+    "import bokeh.plotting\n",
+    "import bokeh.models\n",
+    "import bokeh.palettes\n",
     "\n",
-    "        # absolute indices of this trace\n",
-    "        digit_indices = trace['customdata']\n",
-    "        data_index = digit_indices[trace_index]\n",
+    "title = 'MNIST 2D'\n",
     "\n",
-    "        # convert image to PNG bytes and set image to it\n",
-    "        rawBytes = io.BytesIO()\n",
-    "        plt.imsave(rawBytes, X_high[data_index].reshape((28, 28)), format='png', cmap='gray')\n",
-    "        rawBytes.seek(0)\n",
-    "        img.value = rawBytes.read()\n",
+    "# either INTERACTIVE MODE: directly show in interactive window / notebook\n",
+    "bokeh.plotting.output_notebook()\n",
+    "# or SCRIPT MODE: output to file and open in browser\n",
+    "# filename = os.path.join('images', 'mnist2d.html')\n",
+    "# bokeh.plotting.output_file(filename=filename, title=title)\n",
     "\n",
-    "    fig = go.FigureWidget(data=scatter.data, layout=scatter.layout)\n",
     "\n",
-    "    # figure contains for each color a data trace and for each we have to define the on_hover function\n",
-    "    for fig_data in fig.data:\n",
-    "        fig_data.on_hover(update)\n",
+    "def img_to_base64(img_array):\n",
+    "    assert np.issubdtype(img_array.dtype, 'uint8'), 'Pillow expects an image array to have uint8 format'\n",
+    "    img = PIL.Image.fromarray(img_array.squeeze())\n",
+    "    buffer = io.BytesIO()\n",
+    "    img.save(buffer, format='WebP')\n",
+    "    return 'data:image/webp;base64,' + base64.b64encode(buffer.getvalue()).decode()\n",
     "\n",
-    "    # layout of plot and centered image\n",
-    "    return VBox([fig, Box([img], layout=Layout(display='flex', flex_flow='column', align_items='center'))])\n",
     "\n",
+    "def interactive_scatter_plot(X_high, X_low, y_int):\n",
+    "    \"\"\"\n",
+    "    Make a scatter plot reacting on hover by showing the image\n",
+    "    \"\"\"\n",
+    "    assert X_high.ndim >= 3, 'Original images should be shaped as images e. g. (n_samples, height, width)'\n",
+    "    assert X_low.ndim == 2, 'Reduced dimension should be 2D (n_samples, n_components)'\n",
+    "    assert X_high.shape[0] == X_low.shape[0] == y_int.shape[0], 'Arrays should have the same number of samples'\n",
+    "    assert np.prod(X_high.shape[1:]) > X_low.shape[1], 'First array should be original images, second with reduced dimension'\n",
+    "\n",
+    "    if not isinstance(X_low, pd.DataFrame):\n",
+    "        X_low = pd.DataFrame(X_low, columns=['x', 'y'])\n",
+    "    X_low['class'] = y_int.astype(str)\n",
+    "    X_low['image'] = [img_to_base64(x) for x in X_high]\n",
+    "\n",
+    "    datasource = bokeh.models.ColumnDataSource(X_low)\n",
+    "    color_mapping = bokeh.models.CategoricalColorMapper(\n",
+    "        factors=np.unique(X_low['class']),\n",
+    "        palette=bokeh.palettes.Spectral[X_low['class'].nunique()]\n",
+    "    )\n",
+    "\n",
+    "    plot_figure = bokeh.plotting.figure(\n",
+    "        title=title,\n",
+    "        width=1000,\n",
+    "        height=1000,\n",
+    "        tools=('pan, wheel_zoom, reset, zoom_in')\n",
+    "    )\n",
+    "\n",
+    "    plot_figure.add_tools(bokeh.models.HoverTool(tooltips=\"\"\"\n",
+    "    <div>\n",
+    "        <div>\n",
+    "            <img src='@image' width='96' style='float: left; margin: 5px 5px 5px 5px'/>\n",
+    "        </div>\n",
+    "        <div>\n",
+    "            <span style='font-size: 16px; color: #224499'>Class:</span>\n",
+    "            <span style='font-size: 18px'>@class</span>\n",
+    "        </div>\n",
+    "    </div>\n",
+    "    \"\"\"))\n",
+    "\n",
+    "    plot_figure.scatter(\n",
+    "        'x',\n",
+    "        'y',\n",
+    "        source=datasource,\n",
+    "        color=dict(field='class', transform=color_mapping),\n",
+    "        line_alpha=0.6,\n",
+    "        fill_alpha=0.6,\n",
+    "        size=4\n",
+    "    )\n",
+    "    return plot_figure\n",
     "\n",
     "# load data\n",
-    "(x_train, y_train), (x_test, y_test) = load_data()\n",
+    "(X_train, y_train_int), (X_test, y_test_int) = load_data()\n",
     "\n",
     "# reshape test set to 10 000 x 784\n",
-    "X = x_test.reshape(-1, 28 * 28)"
+    "X = X_test.reshape(-1, 28 * 28)"
    ],
-   "id": "0002-bbdfcc807a677cb51c1861eff7e69e8889bcc9c56d11ca63b972d0d11c7"
+   "id": "0004-5dcaf1cd1786238c4749189d6237574dbb5addb57128548dbe84d900608"
   },
   {
    "cell_type": "markdown",
@@ -88,7 +124,7 @@
     "Wenn Sie mögen ist hier ein Plot von verschiedenen Bildern der gleichen\n",
     "Klasse. So können Sie ein Blick in den Datensatz werfen."
    ],
-   "id": "0003-8683c8202e9f2668b92e0aa78e49a38198e4d638b65ac30ec6020531315"
+   "id": "0005-8683c8202e9f2668b92e0aa78e49a38198e4d638b65ac30ec6020531315"
   },
   {
    "cell_type": "code",
@@ -101,21 +137,21 @@
     "# plot 50 examples for each digit\n",
     "imgs = np.empty((50, 10, 28, 28))\n",
     "for j in range(10):\n",
-    "    imgs[:, j] = x_test[y_test == j][:50]\n",
+    "    imgs[:, j] = X_test[y_test_int == j][:50]\n",
     "\n",
     "fig = px.imshow(imgs, animation_frame=0, facet_col=1, facet_col_wrap=5, binary_string=True)\n",
     "fig.update_xaxes(showticklabels=False)\n",
     "fig.update_yaxes(showticklabels=False)\n",
     "fig.show()"
    ],
-   "id": "0004-c879d58b500c83a0364d8680cc6b05c9cc97d36d3ea3743e112c56644db"
+   "id": "0006-55d5e2344dc9d02ca2bfa1b0c375229ad58016396aa7d7cf5a61050d2d3"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Transformieren Sie die Daten in 2D (ode 3D) und plotten die\n",
-    "Transformierten Daten als Scatter-Plot mit `y_test` als\n",
+    "Transformieren Sie die Daten in 2D (oder 3D) und plotten die\n",
+    "transformierten Daten als Scatter-Plot mit `y_test_int` als\n",
     "Farbunterscheidung. Für PCA ist es schon vorbereitet, aber probieren Sie\n",
     "auch andere Techniken.\n",
     "\n",
@@ -124,7 +160,7 @@
     "mit `from umap import UMAP` importieren und wie jedes andere\n",
     "Scikit-Learn-Modell verwenden.*"
    ],
-   "id": "0006-80f9722cacf3b6426928b4bbdb5fcb32d9d50cc0aaccb011c37c93b7f19"
+   "id": "0008-a567fb8b0d99e19468231f138dc3f8c24df37b96f4a7e1ef8fee53d2cd8"
   },
   {
    "cell_type": "code",
@@ -139,10 +175,10 @@
     "pca = PCA(n_components=2)\n",
     "X_pca = pca.fit_transform(X)\n",
     "\n",
-    "plot = interactive_scatter_plot(X, X_pca, y_test)\n",
-    "display(plot)  # display from IPython.display implicitly in Jupyter imported, which is required anyway"
+    "fig = interactive_scatter_plot(X_test, X_pca, y_test_int)\n",
+    "bokeh.plotting.show(fig)"
    ],
-   "id": "0007-ae5f11cf01a3212244c7bf86638455d0d6a145258c3112beb4c12633405"
+   "id": "0009-8f4cb7ad5313b8b11707eac44fb277bcac2da77b123fc2d06542453784b"
   }
  ],
  "nbformat": 4,