Basics of BSC-CS301 - Numerical Methods, Statistics, Fourier & Fuzzy theory . 

Numerical Methods:

  1. Newton-Raphson is a method to find roots of nonlinear equations.
  2. Error is the difference between the approximate and exact values.
  3. Interpolation estimates values within a set of known data points.
  4. Gauss-Seidel iteratively solves linear equations.
  5. Truncation error occurs when an infinite process is cut short.

Statistics:

  1. Standard deviation measures data spread around the mean.
  2. P-value is the probability of obtaining test results at least as extreme as observed.
  3. Correlation is association; causation implies one event causes another.
  4. Central limit theorem states that sample means form a normal distribution.
  5. Skewness describes data asymmetry from the mean.

Fourier Theory:

  1. Fourier transform converts a signal from time to frequency domain.
  2. Fourier series represents a periodic function as a sum of sines and cosines.
  3. Fourier analysis identifies frequency components in a signal.
  4. Frequency domain shows a signal's spectral components.
  5. Harmonics are multiples of a fundamental frequency.

Fuzzy Theory:

  1. A fuzzy set allows partial membership of elements.
  2. A membership function defines an element’s degree of membership in a fuzzy set.
  3. Fuzzy logic deals with reasoning that is approximate rather than fixed.
  4. Defuzzification converts fuzzy values into a crisp output.
  5. Fuzzy inference draws conclusions based on fuzzy rules and sets.
Check syllabus here.