diff --git a/ML/aufgaben/decisiontree/decisiontree_digits.png b/ML/aufgaben/decisiontree/decisiontree_digits.png
new file mode 100644
index 0000000..94ff5f8
Binary files /dev/null and b/ML/aufgaben/decisiontree/decisiontree_digits.png differ
diff --git a/ML/aufgaben/decisiontree/decisiontree_digits.py b/ML/aufgaben/decisiontree/decisiontree_digits.py
new file mode 100644
index 0000000..37da4cd
--- /dev/null
+++ b/ML/aufgaben/decisiontree/decisiontree_digits.py
@@ -0,0 +1,44 @@
+"""
+Use a decisiontree classifier to predict handwritten digits
+
+- This is an example of a supervised ML algorithm
+    - it has labels on the training data
+    - you tell the model: this is class X during training
+"""
+
+import matplotlib.pyplot as plt
+
+from sklearn import datasets
+from sklearn.tree import DecisionTreeClassifier
+from sklearn import tree
+
+# load the digits dataset
+digits = datasets.load_digits()
+# get a feel for it
+print(digits.data.size)
+print(digits.target.size)
+print(digits.feature_names)
+print(digits.target_names)
+
+# use a decision tree classifier
+# set max_depth to 5 otherwise the tree will get huge
+classifier = DecisionTreeClassifier(max_depth=5)
+# use all but the last sample for training
+classifier.fit(digits.data[:-1], digits.target[:-1])
+
+# use the model to predict the last data sample
+last_sample = digits.data[-1:]
+last_target = digits.target[-1:]
+print(f"predicted: {classifier.predict(last_sample)} vs real: {last_target}")
+
+# print the tree for visual inspection
+fig, ax = plt.subplots(figsize=(20, 10))
+tree.plot_tree(
+    classifier,
+    feature_names=digits.feature_names,
+    class_names=[str(i) for i in digits.target_names],
+    filled=True,
+    rounded=True,
+    ax=ax,
+)
+fig.savefig("decisiontree_digits.png", dpi=500, bbox_inches="tight")
diff --git a/ML/aufgaben/decisiontree/decisiontree_iris.py b/ML/aufgaben/decisiontree/decisiontree_iris.py
index d361715..95118a8 100644
--- a/ML/aufgaben/decisiontree/decisiontree_iris.py
+++ b/ML/aufgaben/decisiontree/decisiontree_iris.py
@@ -19,7 +19,7 @@ print(iris.target.size)
 print(iris.feature_names)
 print(iris.target_names)
 
-# use a decition tree classifier
+# use a decision tree classifier
 classifier = DecisionTreeClassifier()
 # use all but the last sample for training
 classifier.fit(iris.data[:-1], iris.target[:-1])