Difference between revisions of "Matematika datorikiem"
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Kas jāzina datoriķim no matemātikas un teorijas. | Kas jāzina datoriķim no matemātikas un teorijas. | ||
− | Šis nebūt nav izsmeļošs saraksts, bet tēmas un | + | Šis nebūt nav izsmeļošs saraksts, un nav arī sakārtots, |
+ | bet tēmas un atslēgvārdi, ar ko nācies vairākkārt praktiski saskarties. | ||
* Markov model | * Markov model | ||
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* PCA - principal component analysis | * PCA - principal component analysis | ||
* Kalman filter | * Kalman filter | ||
+ | * Particle filter | ||
* Fourier transformation ([http://en.wikipedia.org/wiki/Fourier_transform wikipedia], [http://www.youtube.com/watch?v=BjBb5IlrNsQ Video lekcija no Stanford U.], [http://www.dspguide.com/ch8.htm DSPGuide par DFT]) | * Fourier transformation ([http://en.wikipedia.org/wiki/Fourier_transform wikipedia], [http://www.youtube.com/watch?v=BjBb5IlrNsQ Video lekcija no Stanford U.], [http://www.dspguide.com/ch8.htm DSPGuide par DFT]) | ||
* Standard deviation | * Standard deviation | ||
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* Correlation | * Correlation | ||
* Point spread function | * Point spread function | ||
+ | * Information entropy | ||
+ | * Shannon's theorem (Noisy-channel coding theorem) - ([http://en.wikipedia.org/wiki/Noisy-channel_coding_theorem Wikipedia]) | ||
+ | * Nyquist rate - ([http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem Wikipedia]) |
Latest revision as of 00:43, 8 May 2016
Kas jāzina datoriķim no matemātikas un teorijas.
Šis nebūt nav izsmeļošs saraksts, un nav arī sakārtots, bet tēmas un atslēgvārdi, ar ko nācies vairākkārt praktiski saskarties.
- Markov model
- Hidden Markov model
- Markov chains
- PCA - principal component analysis
- Kalman filter
- Particle filter
- Fourier transformation (wikipedia, Video lekcija no Stanford U., DSPGuide par DFT)
- Standard deviation
- Expected value
- Convolution
- Correlation
- Point spread function
- Information entropy
- Shannon's theorem (Noisy-channel coding theorem) - (Wikipedia)
- Nyquist rate - (Wikipedia)