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Machine Learning

Describe the Connection between BigData and Machine Learning.

In modern world there is a huge amount of labeled and unlabeled data one want to abstract programms from. Machine learning is used for automated data analysis

Machine Learning

Name four subclasses of machine learning.

-supervised learning

-semi-supervised learning

-unsupervised learning

-reinforcement learning

-semi-supervised learning

-unsupervised learning

-reinforcement learning

Machine Learning

What is meant with classification?

Classification means that data is associated to finite predefined classes and there is a correcting instance that ensures correct association.

Machine Learning

What is meant with regression?

Means that the data is labeled to continuous functions, that shows the distribution of values e.g. prediction of prices

Machine Learning

What is clustering?

This is building up classes or finite cluster without a correcting instance even without predefined beliefs or classes

Machine Learning

Dram the Machine Learning Matrix

supv. unsupv.

disc. classif. cluster.

cont. regress. dim.reduct.

disc. classif. cluster.

cont. regress. dim.reduct.

Machine Learning

What means PCA?

PCA is a dimensionality reduction process e.g. Eigenfaces.

Machine Learning

Name the tools of machine learning with each one example.

-probability theory e.g bayes rule

-linear algebra e.g y=ax+n

-optimization e.g minimize f(x)

-computer science e.g programming

-linear algebra e.g y=ax+n

-optimization e.g minimize f(x)

-computer science e.g programming

Machine Learning

What differs deductive from plausible reasoning?

Deductive reasoning is derived from mathematical logic , while plausible reasoning describes that a Possibility can become more or less plausible instead of true or false only.

Machine Learning

What means "modus ponens", what "modus tollens"?

modus ponens: A=>B, A=1 => B=1

modus tollens: A=>B, B=0 => A=0

modus tollens: A=>B, B=0 => A=0

Machine Learning

Name Kolmogorov axioms.

- 0 <= p(A) <= 1

- P(Omega) = 1

- p(A) + p(B) = p(A,B) if A and B are mutually exclusive

- P(Omega) = 1

- p(A) + p(B) = p(A,B) if A and B are mutually exclusive

Machine Learning

Write down Bayes Rule and label all parts of it.

p(H|data) = p(data|H)p(H)/p(data)

posterior belief = likelyhood * prior belief / evidence

posterior belief = likelyhood * prior belief / evidence

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