Machine Learning & Energy at TU Darmstadt

Flashcards and summaries for Machine Learning & Energy at the TU Darmstadt

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Can decision treeds / linear classifiers /kNN model any classification data set perfectly? Which problems would each method have?

Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Given the example: Your predictor says it is a cat, but the actual class is not a cat. How is it called?

Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Given the example: Your predictor says it is not a cat, and the actual class is not a cat. How is it called?

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Learning-Assistant with spaced repetition algorithm

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Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Expain unsupervised learning and supervised learning with one word.

Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Given the example: Your predictor says it is a cat, and the actual class is a cat. How is it called?

Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Why is p(x,y) = p(x|y)p(y)?

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Give a few examples of mathematical models and typical applications with their time scales!

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Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Whats the difference in the meaning of probability ini frequentist and bayesian view?

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What is the difference between supervised and unsupervised learning?

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What is an example for a supervised learning problem with binary and categorical data?

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Which methods are there for forecasting for PV?

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Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Does the (probabilistic) relation between the function values at given data points and desired test points in a Gaussian process model change when the discretization of the function is changed?

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Exemplary flashcards for Machine Learning & Energy at the TU Darmstadt on StudySmarter:

Machine Learning & Energy

Can decision treeds / linear classifiers /kNN model any classification data set perfectly? Which problems would each method have?

No exact representation due to overfitting. Linear models can't represent every data (only with kernel trick), deep enough decision tree can model everything (too deep, very slow), also kNN with only one neighbor

Machine Learning & Energy

Given the example: Your predictor says it is a cat, but the actual class is not a cat. How is it called?

False positive

Machine Learning & Energy

Given the example: Your predictor says it is not a cat, and the actual class is not a cat. How is it called?

True negative 

Machine Learning & Energy

Expain unsupervised learning and supervised learning with one word.

Unsupervised learning: simplify

Supervised learning: predict

Machine Learning & Energy

Given the example: Your predictor says it is a cat, and the actual class is a cat. How is it called?

True positive

Machine Learning & Energy

Why is p(x,y) = p(x|y)p(y)?

If we want to compute the probability of two events we can use the product of the one probability (when the other is assumed) times the probability of the other var

Machine Learning & Energy

Give a few examples of mathematical models and typical applications with their time scales!

-Maxwell eq in lightning protection (ns)

-Electro-magnetic transient models for converter switching (us-s)

-Steady state powerflow for line overloads or voltage feasibility (s-min)

Machine Learning & Energy

Whats the difference in the meaning of probability ini frequentist and bayesian view?

Frequentist:  Frequency of events given infinite
repetition of experiment (flipping coin n times) rather receiving the likelihood (no words about uncertainty of belief

Bayesian: Degree of personal belief (expressing certain uncertainty of experiments and receiving posterior)

Machine Learning & Energy

What is the difference between supervised and unsupervised learning?

In unsupervised learning problems we only have information on the input and no info on the output. As for the supervised learning we also have info on the output.

Machine Learning & Energy

What is an example for a supervised learning problem with binary and categorical data?

Classification

Machine Learning & Energy

Which methods are there for forecasting for PV?

1) Fisheye cameras (1min) -> island grids

2) Satellite images (30-60min)

3) Meteorological data (serveral hours/days)


Machine Learning & Energy

Does the (probabilistic) relation between the function values at given data points and desired test points in a Gaussian process model change when the discretization of the function is changed?

No, because of the properties of the normal distribution the discretization does not change the relation between the function values

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