Introduction To Machine Learning Self-Evaluation Test
Preface
Section 1 - Mathematical background
Multivariate calculus
- take derivatives and integrals;
- derive gradients of multivariate functions.
Linear algebra
- multiply vector and matrices;
- matrix inversion;
- eigenvectors;
- eigenvalues ;
- matrix factorization.
probability and statistics
- mean and variance;
- common probability distribution : Gaussian and Uniform distribution;
- conditional distribution and Bayes rule;
- calculate the likelihood (probability)
- deriving the parameters of the distribution
Section 2 - Usage
If pass "Modest Background Test" , you are good in shape of take the class.
If pass "Minimum Background Test" , but not the "Modest Background Test", then you can take the class but you should expect to devote extra time to fill in necessary math background.
Necessary Minimum Background Test
Multivariate calculus
partial derivative
Vectors and matrices
product
inverse
rank
Probability and statistics
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