Machine Learning at TU München | Flashcards & Summaries

# Lernmaterialien für Machine Learning an der TU München

Greife auf kostenlose Karteikarten, Zusammenfassungen, Übungsaufgaben und Altklausuren für deinen Machine Learning Kurs an der TU München zu.

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What is supervised learning?
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Training samples, with their corresponding targets
--> find a function f that generalizes this relationship!
--> using f, make test predictions a different set of test data
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What is Classification?
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A subclass of supervised learning
--> the targets represent certain categories
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What is Regression?
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A subclass of supervised learning
--> The targets represent continuous numbers!
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What are typical tasks in unsupervised learning?
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• Clustering
• Dimensionality Reduction
• Generative Modeling
• Topic Models
Much more but likely not necessary for the exam
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What is unsupervised learning?
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Find structure in unlabeled data
• Clustering
• Dimensionality reduction
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What is one of the first things to do when you try to analyse new data?
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Visualize them (scattee plots, histograms, ...)
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What is the hard part of decision trees?
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Learning them. Using them is easy and much faster than KNNs for example.

Building the optimal tree is NP-complete!
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How can we decide how many neighbours are best for our KNN?
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Split your data into training, validation and Test Set!
Goal: generalize and pick k so that KNN performs best for unseen data
1. Learn model using the training set
2. Evaluate performance for different ks using the Validation set
3. Report final performance on the test set
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How can we improve the generalization of a 1NN?
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Look at the k-nearest-neighbours & majority vote --> KNN
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What is the F1 Score?
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A score to measure the performance of our model.
The harmonic mean of recall and precision

f1 = (2 * prec * rec)/(prec + rec)
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What is the intuitive core idea of decision trees?
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20-Question-Game
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What can we do instead of just testing all possible combinationd of a decision tree?
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Use greedy, local decisions. What is the best single local decision I can do at this point?
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• 356460 Karteikarten
• 8257 Studierende
• 335 Lernmaterialien

## Beispielhafte Karteikarten für deinen Machine Learning Kurs an der TU München - von Kommilitonen auf StudySmarter erstellt!

Q:
What is supervised learning?
A:
Training samples, with their corresponding targets
--> find a function f that generalizes this relationship!
--> using f, make test predictions a different set of test data
Q:
What is Classification?
A:
A subclass of supervised learning
--> the targets represent certain categories
Q:
What is Regression?
A:
A subclass of supervised learning
--> The targets represent continuous numbers!
Q:
What are typical tasks in unsupervised learning?
A:
• Clustering
• Dimensionality Reduction
• Generative Modeling
• Topic Models
Much more but likely not necessary for the exam
Q:
What is unsupervised learning?
A:
Find structure in unlabeled data
• Clustering
• Dimensionality reduction
Q:
What is one of the first things to do when you try to analyse new data?
A:
Visualize them (scattee plots, histograms, ...)
Q:
What is the hard part of decision trees?
A:
Learning them. Using them is easy and much faster than KNNs for example.

Building the optimal tree is NP-complete!
Q:
How can we decide how many neighbours are best for our KNN?
A:
Split your data into training, validation and Test Set!
Goal: generalize and pick k so that KNN performs best for unseen data
1. Learn model using the training set
2. Evaluate performance for different ks using the Validation set
3. Report final performance on the test set
Q:
How can we improve the generalization of a 1NN?
A:
Look at the k-nearest-neighbours & majority vote --> KNN
Q:
What is the F1 Score?
A:
A score to measure the performance of our model.
The harmonic mean of recall and precision

f1 = (2 * prec * rec)/(prec + rec)
Q:
What is the intuitive core idea of decision trees?
A:
20-Question-Game
Q:
What can we do instead of just testing all possible combinationd of a decision tree?
A:
Use greedy, local decisions. What is the best single local decision I can do at this point?

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